PR Statistics

ONLINE COURSE - Machine Learning using Python (MLUP01) This course will be delivered live
2025-02-10 - 2025-02-14 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, February 10th, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
This course comprehensively introduces Machine Learning, covering theoretical foundations and practical applications. It focuses on crucial machine learning techniques such as supervised and unsupervised learning algorithms, using Python and popular libraries like Scikit-learn, TensorFlow, and Keras. The course emphasises hands-on projects to apply learned concepts to real-world ecological problems. By the end of the course, participants should:

Understand fundamental concepts in machine learning, including supervised and unsupervised learning.
Be able to preprocess data for machine learning tasks.
Understand key algorithms for regression, classification, clustering, and dimensionality reduction.
Gain proficiency in building neural networks and deep learning models.
Be familiar with model selection techniques and hyperparameter tuning.
Have confidence in deploying machine learning models in production environments.
Be able to apply machine learning techniques to solve real-world problems through hands-on projects.

Intended Audiences

Academics and post-graduate students working on machine learning projects.
Data scientists and applied researchers in public or private sectors who need to implement machine learning solutions.
Professionals looking to integrate machine learning into their workflows or enhance their understanding of AI technologies.
Ecologists looking to understand the basic principles of Machine learning and implement them in their research.

Venue
Delivered remotely

Course Information
Time zone – Central Time Zone
Availability – TBC
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session.
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.

Assumed quantitative knowledge
A basic understanding of statistical and mathematical concepts, such as linear algebra.

Assumed computer background
Day one will cover the basics of Python for the module. However, some familiarity with any other programming language is welcome.

Equipment and software requirements
A laptop computer with a working version of Python is required. Python is free and open-source software for PCs, Macs, and Linux computers.
Participants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).

Although not absolutely necessary, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students.
https://www.python.org/downloads/

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 10th
Day 1: A Short Course in Python Basics (9:30 – 17:30)
This day provides participants with the foundational Python skills required for machine learning tasks. This day is designed for beginners or those needing a refresher in Python programming.

Section 1 (Python Essentials for Machine Learning): This section focuses on Python syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
Section 2 (Data Structures and File Handling in Python): Focuses on lists, dictionaries, tuples, sets, and reading/writing files (e.g., CSVs) for data manipulation.

Tuesday 11th
Day 2: Fundamentals of Machine Learning (9:30 – 17:30)
This day focuses on the theoretical foundations of machine learning, detailing the application of learning algorithms in preparation for the practical examples in Python.

Section 3 (Introduction to Machine Learning): This section covers the definition of Machine learning, types of Learning (Supervised, Unsupervised, Reinforcement, Semi-Supervised), applications of Machine Learning and an overview of Python libraries for ML (NumPy, scikit-learn)
Section 4 (Fundamental learning algorithms): This section explores the available learning algorithms and focuses on their applications. We will also discuss the application of different algorithms with practical examples in Ecology.

Wednesday 12th
Day 3: Statistical Learning Theory (9:30 – 17:30)
This day focuses on the theoretical foundations of Statistical Learning Theory (SLT) and illustrates their practical implications.

Section 5 (Important Definitions on SLT): In this section, we will explore the concept of Statistical Learning Theory and its implications for classification tasks in supervised learning settings, highlighting its importance for machine learning practitioners.
Section 6 (Practical implications of the SLT): This section provides a detailed explanation of the practical consequences of statistical learning theory based on Vapniks’ findings and using Support Vector Machines as a helpful example in Python

Thursday 13th
Day 4: Classification boundaries and the power of Deep Neural networks (9:30 – 17:30)
This day introduces participants to the core libraries used in machine learning tasks. scikit-learn is used to implement machine learning algorithms, and TensorFlow is used to build deep learning models.

Section 7 (Classification with various learning algorithms): Offers a step-by-step guide to building learning algorithms using scikit-learn.
Section 8 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.

Friday 14th
Day 5: The Machine Learning Pipeline (9:30 – 17:30)
Participants will learn about the end-to-end workflow of a typical machine learning project using ecological datasets as an illustration.
Section 9 (Preprocessing data and selecting algorithms): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Learning Pipeline will be used.
Section 10 (The Complete Machine Learning Pipeline: From Classification to Evaluating Learning): Covers the end-to-end machine learning workflow, including using the data preprocessed data and creating scikit-learn pipelines to automate critical aspects of the workflow.

Course Instructor

Dr. Gabriel Palma
Gabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University, Ireland, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling, machine vision, machine learning, and applications to ecology and entomology. His personal webpage can be found here
ResearchGateGoogleScholar

Introduction to eco-phylogenetics and comparative analyses using R (ECPHPR)
2025-02-07 | Recorded, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
In this five day course, we provide an introduction to eco-phylogenetics and comparative analyses using R. We begin by providing an overview on the use of phylogenies as a tool for evolutionary biologists and modern techniques to deal with large phylogenies and to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover some of the most relevant eco-phylogenetic analyses and provide examples from the community to themacro-ecological scale (day 2-3). Finally, we introduce a diversity of classic and modern phylogenetic comparative methods to consider the historical relationship of lineages in eco-evolutionary research, including models of trait evolution, analysis of clade diversification and the use of phylogenies in spatial distribution models among others (day 4-5).

Intended Audiences
This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses.

Course Details
Last Up-Dated – 11:02:2021
Duration – Approx. 30 hours
ECT’s – Equal to 3ECT’s
Language – English

Teaching Format
The course will be hands-on and workshop based. Throughout each day, there will be some introductory remarks for each new topic, introducing and explaining key concepts.
The course will take place online using Zoom. On each day, the live video broadcasts will
occur between (UK local time) at:
• 8:00am-10:00am
• 11:00pm-13:00pm
• 14:30pm-16:30pm
All sessions will be video recorded and made available to all attendees.

Assumed quantative knowledge
We will assume general familiarity with the very basics of statistics (e.g. summary statistics, distributions). As this is an introductory course, no phylogenetic background is required.

Assumed computer background
We will assume general familiarity with R elementary operations (e.g. package sourcing, data importing and exporting, object indexing) and some familiarity with programming in R (writing code).

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1
Approx. 7 Hours
• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists, phylogenetic inference (quick overview), phylogenies aevolutionary hypotheses.
• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning, resolving polytomies, sticking species). Visualizing large phylogenies.
• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom, V.PhyloMaker, SUNPLIN and randtip R package.

Day 2
Approx. 7 Hours
• Introduction to the eco-phylogenetic framework, classical conception and posterior modifications.
• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices, null models, applications to biodiversity conservation.
• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity.

Day 3
Approx. 7 Hours
• Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses.
• Spatial phylogenetics. RPD, RPE and CANEPE analysis.
• Overview of functional trait ecology. Functional richness, evenness and divergence.Community weighted means.
• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology

Day 4
Approx. 7 Hours

The phylogenetic comparative method, from independent contrasts to sophisticated modelling.
Analyses of phylogenetic signal and models of evolution: rationale, common- practice, and new trends.
Correlated evolution and ancestral trait reconstruction.
Analyses of diversification, speciation and extinction rates in a geographic context.

Day 5
Approx. 7 Hours

The need to account for phylogenetic relationships in models.
Most common phylogenetic modelling approaches: PGLS, PGLMM, BayesianPMM.
Putting phylogenies in the geography: how to combine phylogenies with species distribution models.

Course Instructor

Rafael Molina Venegas
Works at: Universidad Autónoma de Madrid
Teaches: Introduction to eco-phylogenetics and comparative analyses using R
The scientific career of Rafael Molina Venegas revolves around three research lines pertaining to (1) the ecological and evolutionary mechanisms that jointly shape species assemblages at the community and macroecological scales, (2) the development, improvement, and assessment of phylogenetic methods, and (3) the links between biodiversity and human well-being. While these lines represent clearly differentiated research interests, phylogenetics is a cross-cutting background for all of them. Considering that plants are his true passion in science, he defines himself as a Phylogenetic Plant Ecologist.
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ONLINE COURSE - Machine Vision using Python (MVUP01) This course will be delivered live
2025-02-03 - 2025-02-07 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, February 3rd, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology.
By the end of the course, participants should:

Understand the basic concepts behind the machine vision ecosystem in Python;
Understand the machine vision pipeline workflow;
Understand the application of standard Python packages such as OpenCV and Tensorflow;
Understand the basic concepts behind Deep Neural Networks;
Understand the basic concepts behind Convolutional Deep Neural Networks;
Understand basic concepts behind Transfer learning;
Have the confidence to implement basic Machine vision methods using Python;
Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks;

Intended Audiences

Academics and post-graduate students working on projects related to machine vision
Applied researchers and analysts in public, private or third-sector organisations who need the reproducibility, speed and flexibility of a programming language such as Python for machine vision;
Ecologists utilise Python to solve vision-related problems and look to update their knowledge in the machine vision area.

Venue
Delivered remotely

Course Information
Time zone – Central Time Zone
Availability – TBC
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session.
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.

Assumed quantitative knowledge
A basic understanding of statistical and mathematical concepts. Also, a basic understanding of supervised learning.

Assumed computer background
Day one will cover the basics of Python for the module. However, some familiarity with any other programming language is welcome

Equipment and software requirements
A laptop computer with a working version of Python is required. Python is free and open-source software for PCs, Macs, and Linux computers.
Participants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).

Although not absolutely necessary, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students.
https://www.python.org/downloads/

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 3rd
Day 1: A Short Course in Python Basics (9:30 – 17:30)
This day provides participants with the foundational Python skills required for machine vision tasks. This day is designed for beginners or those needing a refresher in Python programming.

Section 1 (Python Essentials for Machine Vision): This section focuses on Python syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
Section 2 (Data Structures and File Handling in Python): Focuses on lists, dictionaries, tuples, sets, and reading/writing files (e.g., CSVs) for data manipulation.

Tuesday 4th
Day 2: Fundamentals of Computer Vision (9:30 – 17:30)
This day focuses on the theoretical foundations of computer vision, detailing the main aspects.

Section 3 (Introduction to Computer Vision and Image Processing): This section covers the fundamental structure of an image, basic image handling techniques, and an introduction to computer graphics.
Section 4 (Local Image Descriptors and Feature Mapping): This section explores local image descriptors, such as the Harris Corner Detector, and techniques for image-to-image mapping.

Wednesday 5th
Day 3: Fundamentals of Deep Learning (9:30 – 17:30)
This day focuses on the theoretical foundations of deep learning from Neural Networks to Convolutional Neural Networks (CNNs).

Section 5 (Neural Networks: From Basics to Backpropagation): Introduces artificial neurons and explains how neural networks learn through backpropagation.
Section 6 (Convolutional Neural Networks (CNNs) for Image Classification): Provides a detailed explanation of CNN architecture, including convolution layers, pooling layers, and fully connected layers.

Thursday 6th
Day 4: Understanding the Machine Vision Ecosystem in Python (OpenCV & TensorFlow) (9:30 – 17:30)
This day introduces participants to the core libraries used in machine vision tasks. OpenCV is used for image processing, and TensorFlow is used for building deep learning models.

Section 7 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.
Section 8 (Image Processing with OpenCV: Filters, Edge Detection & Contours): Covers basic image manipulation techniques using OpenCV, including resizing, cropping, applying filters (blurring/sharpening), edge detection (Canny), and contour detection.

Friday 7th
Day 5: The Machine Vision Pipeline (9:30 – 17:30)
Participants will learn about the end-to-end workflow of a typical machine vision project.
Section 9 (Preprocessing Images for Deep Learning with OpenCV & TensorFlow): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Vision Pipeline will be used.
Section 10 (The Complete Machine Vision Pipeline: From Image Capture to Classification): Covers the end-to-end machine vision workflow, including image capture, enhancement through preprocessing, segmentation, feature extraction, and classification using machine learning classifiers.

Course Instructor

Dr. Gabriel Palma
Gabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University, Ireland, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling, machine vision, machine learning, and applications to ecology and entomology. His personal webpage can be found here
ResearchGateGoogleScholar

ONLINE COURSE - Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live
2025-01-27 - 2025-02-05 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, January 27th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
In this six-day course, we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes.

Intended Audiences
This course is aimed at anyone who is interested in forecasting methods,and using R for data science or statistics. R is widely used in all areas ofacademic scientific research, and also widely throughout the public, andprivate sector.

Venue
Delivered remotely

Course Information
Time zone – Central Time Zone
Availability – TBC
Duration – 3 days
Contact hours – Approx. 14 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R.Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at: 6pm-9pm
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.

Assumed quantitative knowledge
A basic understanding of R and statistical concepts. Specifically, linear regression models, statistical significance, and hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 27th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 1
Section 1: Introductory concepts in time series analysis. White noise, stationarity, autocovariance and autocorrelation.
Section 2: Useful plots in time series analysis. Time plots, seasonal plots, autocorrelation plots. Time series decomposition: additive and multiplicative using the fable package in R.

Tuesday 28th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 2
Section 3: Time series decomposition: additive and multiplicative using the fable package in R.
Section 4: Benchmark forecasting methods. The naïve, mean, drift, and seasonal naïve methods. Cross-validation methods for time series analysis.
Time series plots (Independant practical 1) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.

Wednesday 29th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Section 4 (‘ctd)
Section 5: Exponential smoothing. Simple exponential smoothing, Holt’s linear method, Holt-Winters seasonalmethod, and fable’s general ETS method.
Time series decomposition and benchmark forecasting methods (Independant practical 2) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.

Monday 3rd
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 4
Section 6: Autoregressive (AR) and moving-average (MA) models. Unit root tests for stationarity. How to identity the order of an AR(p) or an MA(q) model using autocorrelation and partial autocorrelation plots.
Section 7: Autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. Automatic order selection for a (seasonal) ARIMA model using fable. Linear regression with ARIMA errors.
Exponential smoothing (Independant practical 3) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.

Tuesday 4th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 5
Section 8: Generalized Additive Models (GAMs). An introduction to semi-parametric regression using splines. Incorporating trends and seasonal components of a time series using a GAM.
Section 9: An introduction to Bayesian modelling. Implementation of random walks, autoregressive, and moving average models using JAGS.
ARIMA models (Independant practical 4) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.

Wednesday 5th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 6
Section 10: Modelling the variance as a time series process. Autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models. Stochastic volatility models.
Section 11: Continuous time models. Brownian motion and Ornstein-Uhlenbeck processes. Fitting continuous time series models using JAGS.
Section 12: Multivariate time series. Vector autoregression. Simple examples using JAGS.
GAMs and Bayesian models (Independant practical 5) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.

Course Instructor

Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here
ResearchGateGoogleScholarORCIDGitHub

ONLINE COURSE - Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live
2025-01-20 - 2025-01-24 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, January 20th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – Western European Time (Portugal local time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Google Earth Engine (GEE) is a cloud computing platform for processing satellite imagery and other geospatial and observational data. GEE is currently the most complete and efficient platform for performing remote sensing analysis, as it provides access to a large database of satellite imagery and the computational power needed to analyse these images. While other remote sensing programs require the user to have sufficient space and computing power available, all data and processes in GEE are done in the cloud through Google's infrastructure. GEE provides a code editor that works with JavaScript and Python. GEE is the future of remote sensing.
By the end of the course, participants should: Know the catalogue of spatial datasets provided by GEE. Know the most important satellite sensors for environmental studies in GEE. Know how to get remote sensing products from GEE. Process and develop new remote sensing (sub-)products in GEE. Classify satellite imagery in GEE. Perform different types of spatial analyses in satellite imagery in GEE.

Intended Audiences
The target audience for this training is students, researchers, technicians, teachers, or otherprofessionals who work in the areas of remote sensing, environment, geo-informatics, geomatics,geospatial engineering, biology, ecology, and biogeography.

Venue
Delivered remotely

Course Details
Time Zone – Western European Time
Availability – 25 Places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
The topics will be presented and discussed in the theoretical classes to stimulate the interest of theparticipants and to provide the set of knowledge considered necessary and relevant for a completeunderstanding of the Google Earth Engine platform. In the practical classes, participants are invitedto think about and solve a set of problems to consolidate the knowledge acquired in the theoreticallectures. The practical work proposed on the computer aims to train students in solving a set oftypical remote sensing problems related to the topics of the course program. The objective is forparticipants to obtain the necessary tools that will allow them to use Google Earth Engine and todeepen their knowledge autonomously. No data will be provided to the participants because all thenecessary data are stored in GEE.

Assumed quantitative knowledge
Solid knowledge of Geographical Information Systems and Remote Sensing is necessary. This course will suppose the attendees to know how to analyse remote sensing data.

Assumed computer background
No programming experience in JavaScript or Python will be necessary, although having programming knowledge will be extremely useful. However, this is not a course on programming. We will not teach JavaScript or Python but provide the necessary scripts to run analyses in GEE.

Equipment and software requirements
A laptop/personal computer, a list of software you need to install will be sent the week before the course starts.

Tickets

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PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 20th
Classes from 13:00 to 18:00
 Introduction to Google Earth Engine:o Understand the fundamentals of how Google Earth Engine operateso Visualize geospatial information in GEEo Know the catalogue of the most important satellite images in the environmental areao Build the first scripts in GEE and how to run themo GEE related platforms

Tuesday 21st
Classes from 13:00 to 18:00
 Introduction to remote sensing products and analyses in Google Earth Engine:o Select, visualize and access the metadata of satellite image data series (e.g., MODIS, Landsat, Sentinel)o Filter the data by spatial and temporal extentso Aggregate data over time, space and bands using reducer functionso Build image composites and mosaicso Conduct cloud masking operationso Geospatial computations and operations (e.g., obtain terrain products from DEMs and calculate spectral indexes)o Import spatial data as Assets

Wednesday 22nd
Classes from 13:00 to 18:00
 Process, classify and analyse multispectral images:o Quality assessment of satellite imageso Learn the image classification algorithms available in GEEo Perform unsupervised classificationso Photo-interpretation of satellite images and build datasets of training areas (regions of interest)o Conduct spectral separability analyseso Perform supervised classificationso Evaluate final image classificationso Export outputs to Google Drive

Thursday 23rd
Classes from 13:00 to 18:00
 Analyse time-series data and the temporal evolution of vegetation productivity and land useo Calculate spectral indiceso Analyse available and ready-to-use products of vegetation productivity proxies andLULC mapso Build and export time-series plots (e.g., seasonality plots, anomaly analysis)o Conduct non-parametric trend analyses using time-series datao Time series modelling using linear regressionso Export outputs to Google Drive

Friday 24th
Classes from 13:00 to 18:00
 Learn advanced spatial modelling tools:o Import species databases into GEE (vector data or tabular databases) as assetso Search and import environmental variables for modelling procedureso Correlation analyses of environmental variableso Search information about modelling algorithms and classifiers available in GEEo Visualize and calibrate ecological niche models (e.g. Maxent)o Conduct modelling analyses and assess the predictive performance of the modelso Visualize model projections in GEEo Export model outputs to Google Drive

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Dr. Salvador Arenas-Castro

Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology, forestry, spatial ecology, andecoinformatics, all addressed by explicitly considering the spatial component ofecological processes, mainly applying spatially explicit modelling approaches, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com
Google Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro

ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) This course will be delivered live
2024-12-02 - 2024-12-04 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, December 2nd, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
Take your RNA-Seq analysis to the next level with single cell RNA-Seq. This technology allows insights with an unpredicted level of detail, but that brings a new level of complexity to the data analysis. In this course, we will learn about the most popular single cell platforms, how to plan a scRNA-Seq experiment, deal with some of the many pitfalls when analysing your data, and effectively gain exciting, and cell type specific biological insights
By the end of the course participants should:

Understand the basic principles of popular single cell platforms and the pros and cons of the different technologies.
Be able run standard software to process raw 10x Genomics and Parse Bioscience data and interpret the outputs
Understand how to use the ‘Trailmaker’ to quickly analyse scRNA-Seq data.
Understand the basics of the R Bioconductor ‘Seurat’ package, and how to combine it with other tools.
Understand how to perform appropriate data quality control and filtering.
Understand how to cluster cells both within and between samples, and identify possible cell types of individual cells and clusters
Understand how to use statistically robust methods to compare gene expression between sample to identify cell type specific changes in gene expression and potential pathways of interest.

Intended Audiences
Academics, post-graduate students or biotech employees working on, or planning to work on any type of single cell RNA-Seq data.

Venue
Delivered Remotely

Course Details
Availability – 20
Duration – 2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
Mixture of lectures covering the theory, and practical sessions using the Linux command line and RStudio. Practical sessions are a mixture of demonstrations by the tutor and exercises to be completed independently. Data sets for computer practical sessions will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
Participants should have a basic understanding of transcriptomics and molecular biology

Assumed computer background
COMING SOON…

Equipment and software requirements
Participants should have basic experience of R, RStudio and linux.

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1 Classes from 9:30 – 3:30

Basic principles of popular single cell platforms and the pros and cons of the different technologies.
Important considerations when planning a scRNA-Seq experiment.
Running standard software to process raw 10x Genomics and Parse Bioscience data and interpretate outputs to perform an initial assessment of data quality.
Sample and library preparation issues that may affect your data, and how these issues may be detected
Use of the ‘Trailmaker’ tool to perform further analysis without the need for programming skills.

Day 2 Classes from 9:30 – 3:30

Understand the basics of the R Bioconductor ‘Seurat’ package, and how to combine it with other tools.
Normalise data and cluster cells.
Predict cell types of individual cells.
Clean and filter your data in a manner appropriate for your particular sample and tissue type.

Day 3 Classes from 9:30 – 3:30

Integration of and co-clustering of cells from multiple samples.
Identification of cell type marker genes and annotation of clusters.
Use of statistically robust methods to compare gene expression between samples.
Identification cell type specific changes in gene expression and potential pathways of interest.
Discussion of participants individual projects (optional).

Course Instructor

EDINBURGH GENOMICS

Frances Turner

Through my work as a bioinformatician at Edinburgh Genomics, I have many years experience of working with researchers from all areas of life sciences to help them get the most out of their high throughput sequencing data.

read more
This work ranges from bespoke data analysis and one-to-one training, to running popular courses covering a range of applications. My particular focus is on RNA-Seq, especially the exciting opportunities offered by long read and single-cell transcriptomics.

Heleen De Weerd

Heleen de Weerd has been a bioinformatician since 2010, accumulating experience in both industry and academia and working with people from different backgrounds.

read more
Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.

Dr Kathryn Campbell

Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology, she specializes in phylogenetics and viral classification.

read more
Her passion now lies in teaching and outreach, where she brings extensive experience, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics.

ONLINE COURSE - Introduction to Machine Learning using R and Rstudio (IMLR03) This course will be delivered live
2024-11-12 - 2024-11-21 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, November 12th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
In this six-day course, we provide a comprehensive practical and theoretical introduction to statistical machine learning using R. We start by introducing the concepts of supervised and unsupervised learning. We firstly explore unsupervised learning, and introduce k-means andhierarchical clustering, as well as principal components analysis. We then move to supervised learning methods, and cover logistic regression and regularisation methods (such as ridge regression and the LASSO). After that, we introduce the k-nearest neighbours method, and classification and regression trees (CART). Finally, we explore extensions to CART, such as random forests and, if time allows, Bayesian additive regression trees (BART).

Intended Audiences
This course is aimed at anyone who is interested in statistical machine learning methods for clustering, classification or prediction, and using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Information
Time zone – Central Time Zone
Availability – TBC
Duration – 3 days
Contact hours – Approx. 24 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R.Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at: 6pm-9pm
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.

Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R.Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at: 6pm-9pm
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.

Assumed quantitative knowledge
A basic understanding of R and statistical concepts. Specifically, linear regression models, statistical significance, and hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 12th
Classes from 12:00 to 16:00 (Central Time Zone)
Day 1
Section 1: Introductory concepts in statistical machine learning. Unsupervised vs. supervised learning. Useful plots in classification and clustering tasks.
Section 2: Unsupervised learning methods: hierarchical clustering and the k-means method.

Wednesday 13th
Classes from 12:00 to 16:00 (Central Time Zone)
Day 2
Section 3: Dimension reduction techniques and principal components analysis.
Section 4: Regression and classification tasks. Supervised learning methods: linear and logistic regression.

Thursday 14th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Section 5: Tree-based methods. Classification and regression trees (CART), random forests.
Section 6: Extensions to tree-based methods. Bayesian additive regression trees (BART). Combining tree-based methods with a parametric regression framework.

Tuesday 19th
Classes from 12:00 to 16:00 (Central Time Zone)
Day 4
Section 7: Generalized additive models and cross-validation techniques.

Wednesday 20th
Classes from 12:00 to 16:00 (Central Time Zone)
Day 5
Section 8: Tree-based methods. Classification and regression trees (CART), random forests.
Section 9: Extensions to tree-based methods. Bayesian additive regression trees (BART). Boruta.

Thursday 25th
Classes from 12:00 to 16:00 (Central Time Zone)
Day 6
Section 10: Neural networks. Fitting feedforward neural networks and multilayer perceptron using R. Selecting the number of neurons based on cross-validation and information criteria. Neural networks as statistical models.
Section 11: Generalized additive models for location, scale, and shape (GAMLSS). Combining regression trees and neural networks within the GAMLSS regression framework.

Course Instructor

Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here
ResearchGateGoogleScholarORCIDGitHub

ONLINE COURSE – Genome Assembly and Annotation (GAAA01) This course will be delivered live
2024-11-04 - 2024-11-06 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, November 4th, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – UK local time (GMT) – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
Genome assembly is the process of piecing together fragments of DNA to recontruct the original genome. The genome provides crucial information for understanding genetic structure, function and variation.
In recent years, long-read sequencing technologies have revolutionized genome assembly. These long reads can span repetitive sequences and structural variations making genome assembly simpler but also reducing gaps and fragments in the genome, resolve repeats, help with the detection of structural variation as well as improved haplotype phasing.
During this course we will look at data generated using PacBio and Oxford Nanopore, discuss the pros and cons of both sequencing technologies and the effect they might have on genome assembly. During the course we will look at different tools available to generate assemblies, focussing on de novo genome assembly. Polishing using short or long reads and the introduction of Hi-C sequencing can increase completeness of the genomes. At the difference steps during the assembly process we will look at the contiguity, completeness and correctness of the generated genomes, thereby evaluation the status of the genome.
Once a genome has been assembled the next step is annotation. Genome annotation involves identifying and mapping locations of genes and other functional elements within the sequenced genome. We will take a look at the differences between prokaryote and eukaryote genomes and the tools available for annotation. We will talk about steps to improve annotation once the automatic annotation has been made.
By the end of the course, participants should:

Know the difference between Nanopore and PacBio data
Be able to assembly genomes
Be able to assess the generated genomes
Assemble genomes integrating Hi-C data
Know how to annotated a genome

Intended Audiences
Academics and post-graduate students working on projects related to spatial data and applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language

Venue
Delivered Remotely

Course Details
Availability – 22
Duration – 3 days
Contact hours – Approx. 16 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
Intermediate-level lectures interspersed with hands-on mini practicals. Access to Linux VM and data sets for practicals will be provided by the instructors. Time will be available during the course for participants to ask questions regarding their own projects.

Assumed quantitative knowledge
Good familiarity of genomics studies.

Assumed computer background
Good familiarity with Linux will be helpful.

Equipment and software requirements
COMING SOON…

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1 Classes from 10:00 – 15:30Data QC and preprocessing and genome assembly• Data QC and preprocessing using Nanopack• Genome assembly using Redbean, Shasta, Canu• PacBio assembly using hifiasm• Genome evaluation
Day 2 Classes from 10:00 – 15:30Genome polishing and introduction into Hi-C• Polishing created genomes using Racon• Assembly using Hi-C data
Day 3 Classes from 10:00 – 15:30Genome annotation• Genome annotation using Prokka• Look at genome annotation using AUGUSTUS/BRAKER

Heleen De Weerd

Heleen de Weerd has been a bioinformatician since 2010, accumulating experience in both industry and academia and working with people from different backgrounds.

read more
Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.

Urmi Trivedi

Urmi has been working as a Bioinformatician in research support role at Edinburgh Genomics, a sequencing facility within The University of Edinburgh, since 16 years.

read more
She is now leading the Bioinformatics team for almost three years, overseeing a group of experts who are integral to the success of over 100 projects annually.The facility, equipped with cutting-edge sequencing platforms, is at the forefront of genomic research, and Urmi’s team plays a critical role in ensuring the highest standards of data quality and analysis, both for in-house and external projects. Urmi is also a passionate educator, actively involved in designing and delivering training programs that empower the next generation of bioinformaticians. Her area of expertise is Genome assembly, Genome annotation, Metagenomics and metabarcoding.

Dr Kathryn Campbell

Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology, she specializes in phylogenetics and viral classification.

read more
Her passion now lies in teaching and outreach, where she brings extensive experience, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics.

ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live
2024-10-21 - 2024-10-25 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, 21st October, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – `Central European Standard Time (CET) – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
Metabarcoding has emerged as a pivotal technique, rapidly expanding and revolutionizing the way we study biodiversity. From soil samples to aquatic environments, metabarcoding provides insights into the diverse array of organisms present, offering crucial information for conservation efforts and ecological research. However, metabarcoding encounters intrinsic biases inherent in its methodology. Metabarcoding pipelines are designed to mitigate these biases, and this course will offer insights into optimizing these pipelines for accurate and reliable results. With new techniques continuously evolving, we’ll explore methodologies geared towards unraveling both inter and intra-species diversity while addressing the common challenges encountered in a methodology. Additionally, we’ll navigate the landscape of methods enabling comprehensive biodiversity assessments, alongside showcasing new machine learning approaches for inferring ecological quality status. This course will focus on the MJOLNIR3 pipeline and its theoretical framework. This R package is based on eight simple functions divided into four different blocks. For each function, a comprehensive description of the process will be provided, including alternatives from other pipelines and their basic command line usage.

By the end of the course, participants will:

Gain a comprehensive understanding of the theoretical foundations underpinning metabarcoding pipelines.
Develop the ability to identify potential biases and effectively apply specialized software to mitigate them.
Acquire proficiency in working across three distinct levels of coding requirements, encompassing command-line operations and graphical user interface packages.
Demonstrate a thorough comprehension of basic biodiversity analysis techniques, spanning inter and intra-species levels.

Intended Audiences

Academics and post-graduate students engaged in projects associated with DNA metabarcoding.
Applied researchers and environmental managers seeking to implement DNA metabarcoding for ecosystem monitoring purposes.
DNA metabarcoding specialists with expertise in Prokaryotic analysis, seeking to comprehend the specific requisites essential for managing Eukaryotic data.

Venue
Delivered remotely

Course Details
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and refreshers on R usage and linux command line. Intermediate-level lectures interspersed with hands-on mini practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data. Keep in mind that huge datasets can take hours of running time and subsets are recommended. Hands on will try to focus on the different format files to allow students to create their own pipelines.

Assumed quantitative knowledge
A basic understanding of laboratory process. Basic knowledge of biodiversity analysis.

Assumed computer background
Basic familiarity with R and linux command line.

Equipment and software requirements

Participants must use a computer with a good internet connection, a working recent version or R (and ideally also RStudio), and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 21st
Classes from 09:30 – 17:30 (CET)
DAY 1
– What is DNA metabarcoding and how to apply it to my research.– Basic metabarcoding terminology
– Differences between sampling methods– Differences between different target organisms. Universal and specific primers: their pros and limitations.
– COI and other markers.
– Identifying the different biases in laboratory processes. PCR, sequencing, contaminations, quimeras…
– How to manage increasing data volumes.
– Designing a Bias-Handling Strategy. Divide the pipeline in 4 stages:
1- Demultiplexing and initial filters
2- Units delimitation. From Denoising to clustering methods.
3- Taxonomic assignment
4- Final filtering steps
– Industrial assembly lines and Nordic mithology as a metaphores for Metabarcoding pipeline
– Get familiar with basic bash commands and R scripts.

Tuesday 22nd
Classes from 09:30 – 17:30 (CET)
DAY 2
– Presenting MJOLNIR3 pipeline, an R package to easy process metabarcoding data.Getting started with MJOLNIR3 pipeline
– Understand the theory behind
– Presentation and installation of the required software; conda, obitools3, cutadapt, vsearch, DnoisE, SWARM, lulu and dada2.
– Demultiplexing, initial filtering steps, sequence quality, pairing and dereplication and quimera detection.
– Meet the gods RAN, FREYJA and HELA.
– Practical

Wednesday 23rd
Classes from 09:30 – 17:30 (CET)
Day 3
– Alternatives to RAN, FREYJA and HELA
– The dada2 approach.
– To denoise or to cluster.
– Choosing the strategy.
– Meet the god ODIN.
– Practical
– Alternatives to ODIN

Thursday 24th
Classes from 09:30 – 17:30
Day 4
– Alternatives to RAN, FREYJA and HELA
– The dada2 approach.
– To denoise or to cluster.
– Choosing the strategy.
– Meet the god ODIN.
– Hands on
– Alternatives to ODIN

Friday 25th
Classes from 09:30 – 17:30
Day 5
– Taxonomic assignment
– Know the different reference databases
– Meet the god THOR
– ecotag, vsearch and other software
– Practical
– Alternatives to THOR
– Final filtering steps
– Meet the gods FRIGGA, LOKI and face the final battle at the RAGNAROC
– Practical
– Understand the three levels of metabarcoding pipelines. How we go from command line and MJOLNIR3 package to the graphical user interfaces with SLIM as example

Dr. Adrià Antich

Coming soon…
Research Gate Google Scholar ORCID GitHub

ONLINE COURSE – Introduction to Metabarcoding and Metagenomics Analysis (IMAM01) This course will be delivered live
2024-10-07 - 2024-10-10 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, October 7th, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – GMT (Edinburgh Local Time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
Metabarcoding and metagenomics study genetic material recovered from environmental samples. Both methods provide a comprehensive view of microbial communities which are present in various ecosystems. The ability to identify organisms from traces of genetic material in environmental samples has reshaped the way we see life on earth. Especially for microorganisms, metagenomic techniques have granted us unprecedented insight into the microbiome of animals and the environment more broadly
Metabarcoding and metagenomics are both methods to study the composition of these complex communities. Where metabarcoding focusses on looking at a single or a combination of marker genes, metagenomics looks into everything within a community.
During this course we will look at the differences and similarities between these two methods. We explain how to process the data using both short and long reads data, we take a look at the pros and cons and some of the pitfalls. We will guide you through the different approaches to take when processing the data and walk you through using some of the tools which are considered to be golden standard in the field. You will have hands on experience processing real data.
By the end of the course, participants should:

Understand the basic concepts behind metabarcoding and metagenomics
Work with both short and long read data for both metabarcoding and metagenomics
Be able to use Qiime2 and NanoClust for analysis of metabarcoding
Know different methods (metaphlan, humann) for marker based taxonomic and functional annotation of metagenomics data
Create and annotated metagenome assembled genomes (using megahit, checkm, gtdb-tk)
Be able to annotated antibiotic resistance genes in metagenomics data

Intended Audiences
Academics and post-graduate students working on projects related to complex communities and applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language

Venue
Delivered Remotely

Course Details
Availability – 25
Duration – 4 days
Contact hours – Approx. 22 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
Intermediate-level lectures interspersed with hands-on mini practicals. Access to Linux VM and data sets for practicals will be provided by the instructors. Time will be available during the course for participants to ask questions regarding their own projects.

Assumed quantitative knowledge
Some familiarity of metagenomics will be helpful.

Assumed computer background
Good familiarity with Linux will be helpful.

Equipment and software requirements
COMING SOON…

Tickets

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PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1 Classes from 10:00 – 15:30Metabarcoding• Data QC and preprocessing of short reads metabarcoding• Filtering, denoising and assiingment of taxonomy using Qiime2• Align sequencing and build phylogenetic tree• Calculate alpha and beta diversity• Introduction into ANCOM-BC• Long read 16S
Day 2 Classes from 10:00 – 15:30Short read metagenomics• Host removal using KneadData • Taxonomic profiling using MetaPhlan• Functional profiling using HumanN• Antibiotic resistance gene screening
Day 3 Classes from 10:00 – 15:30Short read metagenomics• Metagenome assembly using megahit• Contigs binning and generation of metagenome assembled genomes (MAGs)• De-replication of MAGs• Taxonomic classification of MAGs using GTDB-Tk
Day 4 Classes from 10:00 – 15:30Long read metagenomics• Long reads metagenomics using the DIAMOND-MEGAN pipeline• Data QC and preprocessing of long reads• Metagenome assembly using metaFlye• Functional annotation using Prokka

Course Instructor

EDINBURGH GENOMICS

Heleen De Weerd

Heleen de Weerd has been a bioinformatician since 2010, accumulating experience in both industry and academia and working with people from different backgrounds.

read more
Her expertise spans a wide array of topics with a special interest in genomic and the analysis of highly diverse samples. Since joining Edinburgh Genomics, Heleen has focused on advancements in both short and long reads technologies and application of both to different research questions. She is passionate about sharing her experiences and helping people start their journeys with their data.

Urmi Trivedi

Urmi has been working as a Bioinformatician in research support role at Edinburgh Genomics, a sequencing facility within The University of Edinburgh, since 16 years.

read more
She is now leading the Bioinformatics team for almost three years, overseeing a group of experts who are integral to the success of over 100 projects annually.The facility, equipped with cutting-edge sequencing platforms, is at the forefront of genomic research, and Urmi’s team plays a critical role in ensuring the highest standards of data quality and analysis, both for in-house and external projects. Urmi is also a passionate educator, actively involved in designing and delivering training programs that empower the next generation of bioinformaticians. Her area of expertise is Genome assembly, Genome annotation, Metagenomics and metabarcoding.

Dr Kathryn Campbell

Kathryn recently joined the Edinburgh Genomics team as the Genomics and Bioinformatics Training Coordinator. With a diverse background in bioinformatics and molecular biology, she specializes in phylogenetics and viral classification.

read more
Her passion now lies in teaching and outreach, where she brings extensive experience, engaging with a broad range of audiences. Kathryn is dedicated to empowering learners through comprehensive training, from sample preparation and sequencing to data analysis and interpretation. She is also committed to inspiring the next generation of biologists by working with primary and secondary schools to foster a love for science and genomics.

ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live
2024-09-17 - 2024-09-20 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, September 17th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendeesthrough the accompanying computer practicals via video link, so a good internet connection isessential.
Time Zone
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. However, all sessions will be recorded and made available, allowing attendees from different time zones to follow asynchronously.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Hidden Markov models (HMMs) are flexible statistical models for time series observations driven by underlying states. Over the last decade, HMMs have become increasingly popular within the ecological community as they allow to uncover behavioural state dynamics from noisy sensor data. For example, a typical HMM-based analysis of say GPS locations or acceleration measurements could involve the investigation of internal (e.g. sex, size, age) and external (e.g. temperature, habitat) drivers of behavioural state occupancy.
This workshop will introduce the HMM framework, comprising a mix of theoretical lectures and hands-on practical components using R. In the theoretical sessions, the following topics will be covered: motivation & overview basic HMM formulation fitting an HMM to data model selection & model checking state decoding incorporating covariates, seasonality and random effects other model extensions
These techniques will be illustrated primarily using movement and acceleration data, but are applicable also to other ecological time series data (e.g. capture-recapture). In the practical sessions, we will focus on HMM analyses using the R packages moveHMM and momentuHMM, but will also showcase the use of hmmTMB. Basic knowledge of the free software R is helpful, but not required.
A basic understanding of statistics and probability calculus, as it would be taught in any introductory statistics class, is required. By the end of the course, participants will have a good understanding of what HMMs are and what they can be used for. Participants will also be prepared to tailor a suitable HMM to their data and to implement the corresponding analysis in R.

Intended Audiences
Academics and post-graduate students interested in adding HMMs to their methodological toolbox for analysing ecological data.

Venue
Delivered remotely

Course Details
Availability – 25 places
Duration – 3.5 days
Contact hours – Approx. 24 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
We will spend the mornings to learn about HMM methodology, but will make these lectures asinteractive as possible, with several R code snippets to try out and lots of time for questions. In theafternoons, we will implement example HMM analyses in R. Some example data sets for the practical sessions will be provided by the instructors, but participants are welcome to bring their own data. A Slack channel will be open to discuss any issues for which we may not have enough time in the sessions themselves.

Assumed quantitative knowledge
Basic understanding of statistics and probability calculus (e.g. probability distributions, density functions, conditional probability).

Assumed computer background
Basic familiarity with R is sufficient. In fact, participants will be able to follow most of the workshopwithout prior knowledge of R.

Equipment and software requirements

Participants must use a computer with a good internet connection, a working recent version or R (and ideally also RStudio), and recent versions of some R packages whose installation instructions will be sent a few days before the course. A working webcam is desirable for enhanced interactivity during the live sessions. Some computation power is required for modelling large datasets, although the provided example data (and suggested subsets of participants’ data) can run on an ordinary laptop.

Download R

Download RStudio

Download Zoom

Tickets

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Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 17th
Day 1 – Classes from 08:30 – 16:00
Three 1-hour theory sessions:
 motivation & overview preliminaries: probability calculus & Markov chains the basic HMM formulationA 1-hour practical session: simulating data from an HMM

Wednesday 18th
Day 2 – Classes from 08:30 – 16:00Three 1-hour theory sessions: fitting an HMM to real data, part I fitting an HMM to real data, part II fitting HMMs to movement and acceleration dataA 2-hour practical session: fitting an HMM to real data

Thursday 19th
Day 3 – Classes from 08:30 – 16:00Three 1-hour theory sessions: model selection & model checking state decoding covariatesA 2-hour practical session: complete HMM workflow

Friday 20th
Day 4 – Classes from 08:30 – 11:00Three 1-hour theory sessions: overview of extensions, part I overview of extensions, part II time discuss participants’ own data/questions

Prof. Roland Langrock

Roland is a professor of statistics and data analysis at Bielefeld University in Germany, where he is heavily involved in the teaching of introductory statistics courses as well as advanced statistical methods. His research focuses on statistical method development for state-switching time series models, in particular hidden Markov models, as well as their applications primarily in ecology, sports and economics. Within statistical ecology, he has published extensively on the modelling of animal movement and general behaviour, but also on capture-recapture and distance sampling.Google ScholarHomepage

ONLINE COURSE – Reproducible and collaborative data analysis with R (RACR03) This course will be delivered live
2024-08-27 - 2024-09-05 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday 27th August, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – CET – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course

The computational part of a research is considered reproducible when other scientists (including ourselves in the future) can obtain identical results using the same code, data, workflow and software. Research results are often based on complex statistical analyses which make use of various software. In this context, it becomes rather difficult to guarantee the reproducibility of the research, which is increasingly considered a requirement to assess the validity of scientific claims. Moreover, reproducibility is not only important for findings published in academic journals. It also becomes relevant for sharing analyses within a team, with external collaborators and with one’s supervisor. During this course, the participants will be introduced to a suite of tools they can use in combination with R to make reproducible the computational part of their own research. A strong emphasis is given to collaboration, and participants will learn how to set up a project to work with other people in an efficient way.
At the start off the course, participants learn about the most important aspects that make research reproducible, which go beyond simply sharing R code. This includes problems arising from the use of different packages versions, R versions, and operating systems. The concept of research compendium is introduced and proposed as general framework to organise any research project. The course then moves on to version control with Git and GitHub which are fundamental tools for keeping track of code changes and for collaborating with other people on the same project. We will cover both, basic and more advanced features, like tagging, branching, and merging. Towards the end of the course the participants are introduced to literate programming using Quarto (the new scientific and publishing system recently released by RStudio) with the focus on writing a scientific article or report. The aim is to bind the outputs of the R analysis (i.e. results, tables, and figures) together with the text of the article. Participants will also learn how to use templates to fulfil requirements of different journals.

Intended Audiences
This course is suitable for any MSc and PhD students, postdocs and practitioners from any research field interested in collaborative projects and delivering reproducible results using R.

Venue
Delivered remotely

Course Details
Time zone – CET

Availability – 20 places
Duration – 3 days
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format

On each day, participants will get an introduction to a different tool and practice its use together with the instructor. There will be lecture-style presentations to explain the different problems that make research not reproducible and provide possible solutions to the problem. Lectures will be alternated with hands-on sections guided by the instructor and group exercises to enhance collaboration skills.

Assumed quantitative knowledge

A basic knowledge of statistics is required.

Assumed computer background
The participants are required to have some previous experience with R and should know the main data types and how to run commands to create basic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). Participants should also create a GitHub account in order to attend the second day of this course. Instructions on how to create the account and how to install Git will be provided during the first day.

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 27th
Classes from 17:00-20:00 CET

DAY 1

Intro to the reproducibility crisis
Examples of problems arising from different Operating Systems, R versions, andpackage versions
What happens when you start R
RStudio projects
Project organization

Wednesday 28th
Classes from 17:00-20:00 CET
DAY 2

Code style
Reproducible R environment

Thursday 29th
Classes from 17:00-20:00 CET
DAY 3

Intro to Git and Github
Configure Git and GitHub
Git basic from command line
Create a local repository and push it on Github
Craft a good commit
Clone and fork a GitHub repository

Tuesday 3rd
Classes from 17:00-20:00 CET
DAY 4

Craft a pull request
Git branch, merge, and tag
Git checkout, reset, and revert
Use Git with RStudio
Ignore files

Wednesday 4th
Classes from 17:00-20:00 CET
DAY 5

Literate programming
Quarto to produce html, word, and pdf outputs
Manage references with Zotero
Use templates for word output

Thursday 5th
Classes from 17:00-20:00 CET
DAY 6

Write your scientific article with RMarkdown
Reference tables and figures in the text

Dr. Sergio Vignali

Sergio Vignali is a postdoctoral researcher at the University of Bern (Switzerland), in the division of Conservation Biology of the Institute of Ecology and Evolution. His research focuses on spatial predictive models for animal movements and distributions. Sergio combines his strong scientific interest in animal ecology, particularly birds, with his computational and statistical background to develop new methodological approaches. He is the developer of SDMtune, an R package to tune and evaluate species distribution models. Sergio is also an advocate of open source software and is committed to improving transparency and reproducibility in research.
ResearchGate
GoogleScholar
ORCID
GitHub

ONLINE COURSE - Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ05) This course will be delivered live
2024-05-22 - 2024-05-23 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Wednesday, May 22nd, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
R statistical software is becoming increasingly popular for spatial analysis and mapping. This is partially due to a large number of R packages devoted to applying various spatial methods. These packages, however, are being revised, updated, or even superseded to allow for better performance, simpler user interface, or expanded capabilities. Substantial recent changes in R spatial packages include developing the ‘sf’ package as a successor of ‘sp’, creation of `terra` as a successor of `raster`, and establishing the `stars` package. Additionally, all of these packages were affected by the recent major updates of the PROJ library. In this course, we will learn to use key packages for the analysis of spatial data, both vector (‘sf’) and raster (‘terra’), and see how they differ from their older counterparts, ‘sp’ and ‘raster’. Another important aspect of the course will be to understood spatial projections and coordinate systems, how the recent PROJ changes affect R users, and how to adjust to them.
By the end of the course, participants should:

Understand the basic concepts behind spatial analysis ecosystem in R
Know how packages such as sp/rgeos/rgdal/raster differ from their successors sf/terra/star
Be able to switch from using packages such as sp/rgeos/rgdal/raster to sf/terra/stars
Understood the basic concepts behind spatial projections, and how PROJ.7 differs from PROJ4
Know how to deal with coordinate reference systems in R
Have the confidence to switch from PROJ4 to PROJ7 (i.e., for instance, adjusting old scripts based on PROJ4)?

Intended Audiences

Academics and post-graduate students working on projects related to spatial data
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R for spatial data analysis
Current R users wanting to update your knowledge, including switch from using `sp` to `sf`, and from `raster` to `terra`
The course is designed for intermediate R users interested in understanding modern tools for spatial data analysis in R and R beginners who have prior experience with geographic data and other spatial software.

Course Details
Time zone – Central Europe Standard Time
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
The course will be a mixture of theoretical and practical. Each concept will be first described and explained, and next the attendees will exercise the topics using provided data sets.

Assumed quantitative knowledge
Understanding basic GIS concepts, such as spatial vector, spatial raster, coordinate reference systems would be beneficial, but is not necessary.

Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames. The experience of using some basic R spatial packages, such as sp or raster would be beneficial.
However, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types, and some programming experience, the course may also be appropriate for you.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
​

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Wednesday 22nd
Classes from 09:00 to 17:00Overview of spatial analysis ecosystem in R• available R packages for spatial analysis in R• how do R packages represent spatial objects, and how are they connected with each other• importance of using the more recent R spatial packages, such as ‘sf’ or ‘terra’• main concepts behind map projections (geoids, datums, geographic/projected coordinates, types of projections, etc.)• implementation of these concepts in the PROJ library (used by most R spatial packages)• differences between PROJ.4 and its newer versions (e.g. PROJ.7)Spatial vector data analysis in R• spatial vector data processing & analysis in R• read/write/and visualize spatial vector data• differences between ‘sp’/’rgdal’/’rgeos’ and ‘sf’• moving from ‘sp’ to ‘sf’ for spatial vector data processing & analysis• spherical geometry: how this concept was recently implemented in sf, and what is an impact of this implementation

Thursday 23rd
Classes from 09:00 to 17:00Spatial raster data analysis in R• spatial raster data processing & analysis in R• read/write/and visualize spatial raster data• differences between ‘raster’ and ‘stars’/’terra’• moving from ‘raster’ to ‘terra’ for spatial raster data processing & analysis• short overview of package ‘stars’Coordinate reference systems• how to switch from PROJ.4 to PROJ.7 in R• open session: questions from the participants

Jakub Nowosad
Works at: Adam Mickiewicz University
Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create, collaborate, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book.
ResearchGateGoogleScholarORCIDLinkedInGitHub

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

Teaches

Introduction to spatial analysis of ecological data using R (ISPE)
Making beautiful and effective maps in R (MAPR
Adapting to the recent changes in R spatial packages (sf, terra, PROJ library) (PROJ

ONLINE COURSE – Spatio-Ecological Data Analysis using R and Rstudio (SEAR01) This course will be delivered live
2024-04-22 - 2024-04-25 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, April 22nd, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details
Spatial ecology is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time.
This course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time, due to both human impact and global change. Particular emphasis will be given to: 1) population ecology: how organisms spread in space and how to study it by point pattern analysis, 2) community ecology: how communities are structured and how to study such structure by multivariate analysis; 3) monitoring species distributions and their change in space and time by species distribution modelling; 4) monitoring ecosystem change in space and time by remote sensing data.
The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling.
By the end of the course, participants will:• be able to create their own projects on monitoring of spatial and temporal changes of species and ecosystems at different spatial scales• be able to report in LaTeX and R Markdown the achieved results

Intended Audiences
This course is aimed at academics and post-graduate students working in spatial ecology

Venue
Delivered remotely

Course Information
Time zone – Central European Time
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Theoretical presentations will introduce coding sessions. The whole course is intended to be practical.

Assumed quantitative knowledge
No previous knowledge of R is needed.

Assumed computer background
A basic computer background is needed.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Course packages:– imageRy– overlap– spatstat– terra– vegan

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
info@clovertraining.co.uk

COURSE PROGRAMME

Monday 25th – Classes from 09:30 to 17:30
– R (intro)
[Introduction to the R Software and the Free and Open Source philosophy: how to deal with R making your first code!]
[Spatial R]
– Population Ecology
[Point Patterns Analysis – Spatial statistics: deriving continuous maps from in-situ data, principles of autocorrelation and spatial interpolation]
Tuesday 26th – Classes from 09:30 to 17:30
– Community ecology[Multivariate analysis in R]
[Community niche overlap]
– Remote sensing in R
[Remotely sensed data visualisation]
Wednesday 27th – Classes from 09:30 to 17:30
– Remote sensing in R
[Spectral indices]
[Time series]
Thursday 28th – Classes from 09:30 to 17:30
– External remote sensing data
[Download and use remote sensing data from internet sources]
[Downloading and visualising Copernicus data]
– Image data processing
[Remotely sensed data classification: land cover maps]
[Ecosystem variability]
[Multivariate analysis on remotely sensed data]
Friday 29th – Classes from 09:30 to 17:30
– Reporting
[LaTeX for scientific reporting via articles]
[LaTeX/Beamer for scientific reporting via presentations]
[R Markdown for scientific reporting via internet pages]

Course Instructor

Dr. Duccio Rocchini
Coming soon…

In Person Course - Advanced Python for Biologists - University of Glasgow
2024-04-15 - 2024-04-26 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, April 15th, 2024

This course is being deliver by Martin Jones @ Python for Biologists, you can book via his website here
https://www.polyomics.gla.ac.uk/course-python_course_APR24.html

ONLINE COURSE - Advancing in R (ADVR01) This course will be delivered live
2024-03-25 - 2024-03-29 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, March 25th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
COURSE DETAILS
This course is designed to provide attendees with a comprehensive understanding of
statistical modelling and its applications in various fields, such as ecology, biology, sociology,
agriculture, and health. We cover all foundational aspects of modelling, including all coding
aspects, ranging from data wrangling, visualisation and exploratory data analysis, to
generalized linear mixed models, assessing goodness-of-fit and carrying out model
comparison.
Data wrangling
For data wrangling, we focus on tools provided by R's tidyverse. Data wrangling is the art of
taking raw and messy data and formatting and cleaning it so that data analysis and
visualization may be performed on it. Done poorly, it can be a time consuming, laborious,
and error-prone. Fortunately, the tools provided by R's tidyverse allow us to do data
wrangling in a fast, efficient, and high-level manner, which can have dramatic consequence
for ease and speed with which we analyse data. We start with how to read data of different
types into R, we then cover in detail all the dplyr tools such as select, filter, mutate, and
others. Here, we will also cover the pipe operator (%>%) to create data wrangling pipelines
that take raw messy data on the one end and return cleaned tidy data on the other. We
then cover how to perform descriptive or summary statistics on our data using dplyr’s
group_by and summarise functions. We then turn to combining and merging data. Here, we
will consider how to concatenate data frames, including concatenating all data files in a
folder, as well as cover the powerful SQL-like join operations that allow us to merge
information in different data frames. The final topic we will consider is how to “pivot” data
from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider
functions.
Data visualisation
For visualisation, we focus on the ggplot2 package. We begin by providing a brief overview
of the general principles data visualization, and an overview of the general principles behind
ggplot. We then proceed to cover the major types of plots for visualizing distributions of
univariate data: histograms, density plots, barplots, and Tukey boxplots. In all of these
cases, we will consider how to visualize multiple distributions simultaneously on the same
plot using different colours and "facet" plots. We then turn to the visualization of bivariate
data using scatterplots. Here, we will explore how to apply linear and nonlinear smoothing
functions to the data, how to add marginal histograms to the scatterplot, add labels to
points, and scale each point by the value of a third variable. We then cover some additional
plot types that are often related but not identical to those major types covered during the
beginning of the course: frequency polygons, area plots, line plots, uncertainty plots, violin
plots, and geospatial mapping. We then consider more fine grained control of the plot by
changing axis scales, axis labels, axis tick points, colour palettes, and ggplot "themes".
Finally, we consider how to make plots for presentations and publications. Here, we will introduce how to insert plots into documents using RMarkdown, and also how to create
labelled grids of subplots of the kind seen in many published articles.
Generalized linear models
Generalized linear models are generalizations of linear regression models for situations
where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The
specific models we cover include binary, binomial, and categorical logistic regression,
Poisson and negative binomial regression for count variables, as well as extensions for
overdispersed and zero-inflated data. We begin by providing a brief overview of the normal
general linear model. Understanding this model is vital for the proper understanding of how
it is generalized in generalized linear models. Next, we introduce the widely used binary
logistic regression model, which is is a regression model for when the outcome variable is
binary. Next, we cover the binomial logistic regression, and the multinomial case, which is
for modelling outcomes variables that are polychotomous, i.e., have more than two
categorically distinct values. We will then cover Poisson regression, which is widely used for
modelling outcome variables that are counts (i.e the number of times something has
happened). We then cover extensions to accommodate overdispersion, starting with the
quasi-likelihood approach, then covering the negative binomial and beta-binomial models
for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson
and negative binomial models, which are for count data with excessive numbers of zero
observations.
Mixed models
We will focus primarily on multilevel linear models, but also cover multilevel generalized
linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling.
We will begin by focusing on random effects multilevel models. These models make it clear
how multilevel models are in fact models of models. In addition, random effects models
serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models.
In this coverage of random effects, we will also cover the important concepts of statistical
shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to
cover linear mixed effects models, particularly focusing on varying intercept and/or varying
slopes regression models. We will then cover further aspects of linear mixed effects models,
including multilevel models for nested and crossed data data, and group level predictor
variables. Towards the end of the course we also cover generalized linear mixed models
(GLMMs), how to accommodate overdispersion through individual-level random effects, as
well as Bayesian approaches to multilevel levels using the brms R package.
Model selection and model simplification
Throughout the course we consider the fundamental issue of how to measure model fit and
a model’s predictive performance, and discuss a wide range of other major model fit
measurement concepts like likelihood, log likelihood, deviance, and residual sums of
squares. We thoroughly explore nested model comparison, particularly in general and
generalized linear models, and their mixed effects counterparts. We discuss out-of-sample
generalization, and introduce leave-one-out cross-validation and the Akaike Information Criterion (AIC). We also cover general concepts and methods related to variable selection,
including stepwise regression, ridge regression, Lasso, and elastic nets. Finally, we turn to
model averaging, which may represent a preferable alternative to model selection.

Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Information
Time zone – GMT+1
Availability – TBC
Duration – 3 x 1/2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at:
• 10am-12pm
• 1pm-3pm
• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub

Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 25th
Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00
Day 1
Topic 1: Reading in data. We will begin by reading in data into R using tools such
as readr and readxl. Almost all types of data can be read into R, and here we will consider
many of the main types, such as csv, xlsx, sav, etc. Here, we will also consider how to control
how data are parsed, e.g., so that they are read as dates, numbers, strings, etc.
Topic 2: Wrangling with dplyr. We will next cover the very powerful dplyr R package. This
package supplies a number of so-called "verbs" — select, rename, slice, filter, mutate, arrange, etc. — each of which focuses on a key data manipulation tools, such as selecting or changing variables. All of these verbs can be chained together using "pipes" (represented by %>%). Together, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here, we will also learn about the key concept of "tidy data", which is roughly where each row of a data frame is an observation and each column is a variable.
Topic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with
great effect to summarize data using descriptive statistics.
Topic 4: Merging and joining data frames. There are multiple ways to combine data frames,
with the simplest being "bind" operations, which are effectively horizontal or vertical
concatenations. Much more powerful are the SQL-like "join" operations. Here, we will
consider the inner_join, left_join, right_join, full_join operations. In this section, we will also
consider how to use purrr to read in and automatically merge large sets of files.
Topic 5: Pivoting data. Sometimes we need to change data frames from "long" to "wide"
formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this.

Tuesday 26th
Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00
Day 2
Topic 1: What is data visualization. Data visualization is a means to explore and understand
our data and should be a major part of any data analysis. Here, we briefly discuss why data
visualization is so important and what the major principles behind it are.
Topic 2: Introducing ggplot. Though there are many options for visualization in R, ggplot is
simply the best. Here, we briefly introduce the major principles behind how ggplot works,
namely how it is a layered grammar of graphics.
Topic 3: Visualizing univariate data. Here, we cover a set of major tools for visualizing
distributions over single variables: histograms, density plots, barplots, Tukey boxplots. In each case, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots.
Topic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data.
Here, in addition to covering how to visualize multiple groups using colours and facets, we
will also cover how to provide marginal plots on the scatterplots, labels to points, and how
to obtain linear and nonlinear smoothing of the plots.
Topic 5: More plot types. Having already covered the most widely used general purpose
plots, we now turn to cover a range of other major plot types: frequency polygons, area
plots, line plots, uncertainty plots, violin plots, and geospatial mapping. Each of these are
important and widely used types of plots, and knowing them will expand your repertoire.
Topic 6: Fine control of plots. Thus far, we will have mostly used the default for the plot
styles and layouts. Here, we will introduce how to modify things like the limits and scales on
the axes, the positions and nature of the axis ticks, the colour palettes that are used, and
the different types of ggplot themes that are available.
Topic 7: Plots for publications and presentations. Thus far, we have primarily focused on
data visualization as a means of interactively exploring data. Often, however, we also want
to present our plots in, for example, published articles or in slide presentations. It is simple
to save a plot in different file formats, and then insert them into a document. However, a
much more efficient way of doing this is to use RMarkdown to run the R code and
automatically insert the resulting figure into a, for example, Word document, pdf document,
html page, etc. In addition, here we will also cover how to make labelled grids of subplots
like those found in many scientific articles.

Wednesday 27th
Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00
Day 3
Topic 1: The general linear model. We begin by providing an overview of the normal, as in
normal distribution, general linear model, including using categorical predictor variables.
Although this model is not the focus of the course, it is the foundation on which generalized
linear models are based and so must be understood to understand generalized linear
models.
Topic 2: Binary logistic regression. Our first generalized linear model is the binary logistic
regression model, for use when modelling binary outcome data. We will present the
assumed theoretical model behind logistic regression, implement it using R’s glm, and then
show how to interpret its results, perform predictions, and (nested) model comparisons.
Topic 3: Binomial logistic regression. Here, we show how the binary logistic regression can
be extended to deal with data on discrete proportions. We will also present alternative link
functions to the logit, such as the probit and complementary log-log links.
Topic 4: Categorical logistic regression. Categorical logistic regression, also known as multinomial logistic regression, is for modelling polychotomous data, i.e. data taking more than two categorically distinct values. Categorical logistic regression is based on an extension of the binary logistic regression case.
Topic 5: Poisson regression. Poisson regression is a widely used technique for modelling
count data, i.e., data where the variable denotes the number of times an event has occurred.

Thursday 28th
Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00
Topic 1: Measuring model fit. Here, the concept of conditional probability of the observed
data, or of future data, is of vital importance. This is intimately related, though distinct, to
concept of likelihood and the likelihood function, which is in turn related to the concept of
the log likelihood or deviance of a model. Here, we also show how these concepts are
related to concepts of residual sums of squares, root mean square error (rmse), and
deviance residuals.
Topic 2: Nested model comparison. In this section, we cover how to do nested model
comparison in general linear models, generalized linear models, and their mixed effects
(multilevel) counterparts. First, we precisely define what is meant by a nested model. Then
we show how nested model comparison can be accomplished in general linear models with
F tests, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear
models, we can accomplish nested model comparison using deviance based chi-square tests
via Wilks’s theorem.
Topic 3: Overdispersion models. The quasi-likelihood approach for both the Poisson and
binomial models. Negative binomial regression. The negative binomial model is, like the
Poisson regression model, used for unbounded count data, but it is less restrictive than
Poisson regression, specifically by dealing with overdispersed data. Beta-binomial
regression. The beta-binomial model is an overdispersed alternative to the binomial.
Topic 4: Zero inflated models. Zero inflated count data is where there are excessive
numbers of zero counts that can be modelled using either a Poisson or negative binomial
model. Zero inflated Poisson or negative binomial models are types of latent variable
models.
Topic 5: Random effects models. The defining feature of multilevel models is that they are
models of models. We begin by using a binomial random effects model to illustrate this.
Specifically, we show how multilevel models are models of the variability in models of
different clusters or groups of data.
Topic 6: Normal random effects models. Normal, as in normal distribution, random effects
models are the key to understanding the more general and widely used linear mixed effects
models. Here, we also cover the key concepts of statistical shrinkage and intraclass
correlation.

Friday 29th
Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00
Day 5
Topic 1: Out of sample predictive performance: cross validation and information criteria.
Here, we describe how to measure out of sample predictive performance, which measures
how well a model can generalize to new data. This is arguably the gold-standard for
evaluating any statistical models. A practical means to measure out of sample predictive
performance is cross-validation, especially leave-one-out cross-validation. Leave-one-out
cross-validation can, in relatively simple models, be approximated by Akaike Information
Criterion (AIC), which can be exceptionally simple to calculate. We will discuss how to
interpret AIC values, and describe other related information criteria, some of which will be
used in more detail in later sections.
Topic 2: Linear mixed effects models. Next, we turn to multilevel linear models, also known
as linear mixed effects models. We specifically deal with the cases of varying intercept
and/or varying slope linear regression models.
Topic 3: Multilevel models for nested data. Here, we will consider multilevel linear models
for nested, as in groups of groups, data. As an example, we will look at multilevel linear
models applied to data from students within classes that are themselves within different
schools, and where we model the variability of effects across the classes and across the
schools.
Topic 4: Multilevel models for crossed data. In some multilevel models, each observation
occurs in multiple groups, but these groups are not nested. For example, animals may be
members of different species and in different locations, but the species are not subsets of
locations, nor vice versa. These are known as crossed or multiclass data structures.
Topic 5: Group level predictors. In some multilevel regression models, predictor variable are
sometimes associated with individuals, and sometimes associated with their groups. In this
section, we consider how to handle these two situations.
Topic 6: Generalized linear mixed models (GLMMs). Here, we extend the linear mixed model
to the exponential family of distributions and showcase an example using the Poisson
GLMM. We also cover how to accommodate overdispersion through individual-level
random effects.
Topic 7: Bayesian multilevel models. All of the models that we have considered can be
handled, often more easily, using Bayesian models. Here, we provide an brief introduction
to Bayesian models and how to perform examples of the models that we have considered
using Bayesian methods and the brms R package.
Topic 8: Variable selection. Variable selection is a type of nested model comparison. It is
also one of the most widely used model selection methods, and variable selection of some
kind is almost always done routinely in all data analysis. In particular, we cover stepwise
regression (and its limitations), all subsets methods, ridge regression, Lasso, and elastic nets.
Topic 9: Model averaging. Rather than selecting one model from a set of candidates, it is
arguably always better perform model averaging, using all the candidates models, weighted by the predictive performance. We show how to perform model average using information
criteria.

Course Instructor

Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here
ResearchGate
GoogleScholar
ORCID
GitHub

ONLINE COURSE - An Introduction to Spatial Eco-Phylogenetics and Comparative Methods (SECM01) This course will be delivered live
2024-03-19 - 2024-03-22 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, March 19th, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM
TIME ZONE – CET (Central European Time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course

In this course we introduce phylogenetic analyses in a spatial context. Phylogenetic analyses often imply a high number of species for which phylogenetic information is unavailable, hence we begin by providing an overview on modern techniques to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover the most popular analyses in the spatial phylogenetics discipline (day 2), with particular focus on the canonical analysis of neo- and paleo-endemism (CANAPE). The second part of the course will be devoted to integrating phylogenetic information into models of geographic distribution of species (day 3), followed by an overview of recent advances to improve ecological forecasts using phylogenetic mixed models in a Bayesian framework (day 4).

By the end of the course, participants should:

Know how to expand incomplete phylogenies based on taxonomic information and customizing simulation parameters for optimal expansion.
Understand the metrics and concepts used in spatial phylogenetics (i.e. phylogenetic alpha and beta diversity, phylogenetic endemism), interpret them critically, and assess pros and cons of analytical techniques.
Calculate phylogenetic predictors that can be included as covariates in Species Distribution or Niche Models.
Understand and implement the phylogenetic mixed model (PMM) and translate its predictions into a spatial context.

Intended Audiences
This course is aimed at anyone who wishes to introduce into spatial phylogenetics and comparative analyses (in general and within a spatial context in particular)

Course Details
Venue – Delivered remotelyAvailability – 30 placesDuration – 5 daysContact hours – Approx. 35 hoursECT’s – Equal to 3 ECT’sLanguage – English

Teaching Format
The course will be hands-on and workshop based. Throughout each day, there will be some introductory remarks for each new topic, introducing and explaining key concepts.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared with the attendees.

Assumed quantative knowledge

We will assume general familiarity with the very basics of statistics (e.g. summary statistics, distributions). As this is an introductory course, no phylogenetic background is required.

Assumed computer background
We will assume general familiarity with R elementary operations (e.g. package sourcing, data importing and exporting, object indexing) and some familiarity with programming in R (writing code).

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 19th
Classes from 8:00 to 13:00 CET
DAY 1
Expansion of molecular trees using taxonomic information and fundamental metrics of phylogenetic structure

Software for tree expansion exercises; randtip, PhyloMaker
An overview of the fundamental metrics of phylogenetic structure. Null models.

Wednesday 20th
Classes from 8:00 to 13:00 CET
DAY 2
Spatial Phylogenetics

Canonical analysis of neo- and paleo- endemism. Metrics, rationale, workflow, and implementation.

Thursday 21st
Classes from 8:00 to 13:00 CET
DAY 3
Phylogenetic Species Distribution Models

Putting phylogenies in the geography: the imprints of evolutionary relationships in distribution models.
Combining phylogenies with co-occurrence to infer spatial phylogenetic predictors.
Fitting, evaluating and interpreting Phylogenetic-SDMs.

Friday 22nd
Classes from 8:00 to 13:00 CET
DAY 4
Beyond PGLS – Bayes and more

Most common phylogenetic modelling approaches: PGLS
PGLMM
The phylogenetic mixed model (PMM) in a Bayesian framework

Course Instructor

Dr. Rafael Molina Venegas
The scientific career of Rafael Molina Venegas revolves around three research lines pertaining to (1) the ecological and evolutionary mechanisms that jointly shape species assemblages at the community and macroecological scales, (2) the development, improvement, and assessment of phylogenetic methods, and (3) the links between biodiversity and human well-being. While these lines represent clearly differentiated research interests, phylogenetics is a cross-cutting background for all of them. Considering that plants are his true passion in science, he defines himself as a Phylogenetic Plant Ecologist. I personal page can be found here
ResearchGateGoogleScholar

Course Instructor

Dr. Morales Castilla Ignacio
Dr. Morales-Castilla is a biogeographer and macroecologist interested in the spatial-temporal distribution of biodiversity. His research program aims to: (1) disentangle the relative roles of evolution and ecology as drivers of community structure, (2) understand how different aspects of the species’ niches are evolutionarily conserved and, (3) enhance models of biotic interactions and/or species distributions by integrating phylogenetic, functional and geographic information. You can check his publication record at the links provided above. You can find hiss homepage here
ResearchGateGoogleScholarORCIDGitHub

CURSO ONLINE – Introdução a Modelos Mistos usando R e R Studio (IMMR08) Este curso será ministrado ao vivo
2024-03-12 - 2024-03-14 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Data do Evento
Terça-feira, 12th Março, 2024

FORMATO DO CURSO
Este é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial.
PROGRAMA
FUSO HORÁRIO – Horário de Brasília – porém, todas as sessões serão gravadas e disponibilizadas online, permitindo que participantes de outros fusos horários também acompanhem.
Por favor, envie um email para oliverhooker@prstatistics.com para maiores detalhes, ou para discutir como Podemos acomodá-lo(a).

DETALHES DO CURSO

Este curso fornece uma introdução teórica e prática a modelos mistos, também conhecidos como modelos multi-nível ou hierárquicos. Nosso foco primário será em modelos lineares mistos, porém também cobriremos modelos lineares generalizados mistos. Também descreveremos abordagens Bayesianas para modelos mistos. Começaremos com modelos de efeitos aleatórios. Esses modelos mostram, com clareza, como os modelos mistos são, na verdade, “modelos de modelos”. Também, modelos de efeitos aleatórios servem como uma base sólida para auxiliar o entendimento de modelos mistos. Nós também trataremos de conceitos importantes relacionados a shrinkage, ou “redução/encolhimento” dos efeitos aleatórios, e correlação intraclasse. Então, cobriremos modelos lineares mistos, com foco particular em modelos de intercepto e/ou coeficientes angulares aleatórios. Depois, cobriremos modelos mistos para dados com estrutura aninhada ou cruzada, bem como preditores de nível de grupo. Então, trataremos de modelos lineares generalizados mistos e como utilizar efeitos aleatórios a nível observacional para acomodar superdispersão. Por fim, cobriremos uma breve introdução à abordagem Bayesiana por meio do pacote brms.

PÚBLICO ALVO

Este curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica, bem como nos setores público e privado.

LOCAL
Ministrado remotamente.

NFORMAÇÃO DO CURSO
Fuso horário – Horário de Brasília
Disponibilidade – A definir
Duração – 3 x 1/2 dias
Horas de contato – Aprox. 12 horas
Créditos – Equivalente a 1 crédito
Idioma – Português

FORMATO DE ENSINO
Este curso será um workshop prático. Para cada tópico, haverá uma apresentação estilo aula, isto é, utilizando slides ou lousa eletrônica, para introduzir conceitos-chaves e teoria. Então, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões.
No início de cada dia, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários.
Embora não seja estritamente necessário, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente.
Todas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha.
Todos os materiais, como slides, conjuntos de dados, etc., serão compartilhados via GitHub.

CONHECIMENTO QUANTITATIVO NECESSÁRIO

Um entendimento básico de conceitos estatísticos chaves. Especificamente, modelos de regressão linear, significância estatística e testes de hipóteses.

CONHECIMENTO COMPUTACIONAL NECESSÁRIO
Familiaridade com o R. Importar/exportar dados, manipular data frames, ajustar modelos estatísticos básicos e gerar gráficos simples.

REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE

Um computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC, Mac e Linux.Participantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor, certifique-se de que você tem direitos de administrador em seu computador).Um monitor grande e uma segunda tela, embora não seja absolutamente necessário, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Faça o download do R

Faça o download do RStudio

Faça o download do Zoom

Tickets

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Tickets are no longer available

POR FAVOR, LEIA – POLÍTICA DE CANCELAMENTO
Cancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados, contate oliverhooker@prstatistics.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos, um reembolso completo das taxas do curso será creditado.

Se você estiver incerto em relação à adequabilidade do curso, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com

PROGRAMA DO CURSO

Terça-feira 12th
Aulas das 14:00 às 18:00 (Horário de Brasília)
DIA 1

Tópico 1: Modelos de efeitos aleatórios. A característica que define modelos mistos é o fato de que eles são “modelos de modelos”. Começaremos utilizando modelos binomiais de efeitos aleatórios para ilustrar esse conceito. Especificamente, mostramos como modelos mistos são modelos da variabilidade em modelos de diferentes clusters, ou grupos de dados.

Tópico 2: Modelo normal de efeitos aleatórios. Esses são modelos chaves para entender o modelo misto de uma maneira mais geral. Aqui, cobriremos os conceitos estatísticos de shrinkage e de correlação intraclasse.

Quarta-feira 13th
Aulas das 14:00 às 18:00 (Horário de Brasília)
Tópico 3: Modelo linear misto. Agora, cobriremos os modelos lineares mistos. Lidamos, especificamente, com os casos de interceptos e/ou coeficientes angulares aleatórios.
Tópico 4: Modelos mistos para dados com estrutura aninhada. Aqui, consideramos modelos para dados com estrutura aninhada, isto é, grupos de grupos. Como um exemplo, aplicaremos modelos mistos a dados de estudantes dentro de classes dentro de escolas, onde modelamos a variabilidade dos efeitos entre classes e entre escolas.
Tópico 5: Modelos mistos para dados com estrutura cruzada. Em alguns modelos mistos, cada observação ocorre em múltiplos grupos, que não estão aninhados. Por exemplo, animais podem ser membros de diferentes grupos taxonômicos e em diferentes locais, mas os grupos taxonômicos não são subconjuntos dos locais, ou vice-versa.

Quinta-feira 14th
Aulas das 14:00 às 18:00 (Horário de Brasília)
Tópico 6: Preditores a nível de grupo. Em alguns modelos mistos, variáveis preditoras podem estar associadas a indivíduos ou a grupos. Nesta seção, consideramos como lidar com essas duas situações.
Tópico 7: Modelos lineares generalizados mistos. Aqui, estendemos o modelo linear misto para a família exponencial de distribuições e mostramos um exemplo usando o MLG misto Poisson. Também abordamos como acomodar superdispersão por meio de efeitos aleatórios a nível individual.
Tópico 8: Modelos mistos Bayesianos. Todos os modelos considerados podem ser ajustados utilizando a abordagem Bayesiana. Aqui, fornecemos uma breve introdução a modelos Bayesianos e como ajustar os modelos mistos que consideramos durante o curso utilizando o pacote brms.

Instrutor do curso

Dr. Rafael De Andrade Moral
Rafael é Professor Associado de Estatística na Maynooth University, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia, manejo da fauna silvestre, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui.
ResearchGateGoogleScholarORCIDGitHub

CURSO ONLINE – Introdução a Modelos Lineares Generalizados usando R e R Studio (IGLM07) Este curso será ministrado ao vivo
2024-02-20 - 2024-02-22 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Data do Evento
Terça-feira, 20th Fevereiro, 2024

FORMATO DO CURSO
Este é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial.
PROGRAMA
FUSO HORÁRIO – Horário de Brasília – porém, todas as sessões serão gravadas e disponibilizadas online, permitindo que participantes de outros fusos horários também acompanhem.
Por favor, envie um email para oliverhooker@prstatistics.com para maiores detalhes, ou para discutir como Podemos acomodá-lo(a).

DETALHES DO CURSO
Este curso fornece uma introdução teórica e prática aos modelos lineares generalizados usando o R. Modelos lineares generalizados (MLGs) são generalizações de modelos de regressão linear para situações em que a variável resposta é, por exemplo, binária, ou categórica, ou de contagem, etc. Os modelos específicos que apresentaremos incluem regressão logística binária, binomial e categórica, regressão Poisson e binomial negativa para variáveis de contagem. Também apresentaremos modelos de regressão de Poisson e binomial negativo inflacionados de zeros. Começaremos com uma breve recapitulação do modelo linear normal. Entender esse modelo é vital para um entendimento apropriado de como ele pode ser generalizado na teoria dos MLGs. Depois, introduziremos o modelo de regressão logística binário amplamente utilizado, que é um modelo de regressão para quando a variável resposta é binária. Depois, apresentamos o caso da regressão logística binomial (duas categorias), e por fim multinomial, para modelar respostas politômicas, isto é, que podem integrar mais de duas categorias. Depois apresentaremos a regressão Poisson, que é amplamente utilizada para modelar variáveis respostas de contagem (isto é, o número de vezes que algo aconteceu). Depois apresentaremos modelos de superdispersão, que acomodam uma variabilidade maior do que a esperada pelos modelos de Poisson e binomial. Apresentaremos os modelos de quase-verossimilhança, binomial negativo e beta-binomial, para dados de contagens e proporções discretas, respectivamente. Por fim, apresentaremos modelos de Poisson e binomial negativo inflacionados de zeros, para dados de contagem com um excesso de observações nulas.

PÚBLICO ALVO
Este curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica, bem como nos setores público e privado.

LOCAL
Ministrado remotamente.

NFORMAÇÃO DO CURSO
Fuso horário – Horário de Brasília
Disponibilidade – A definir
Duração – 3 x 1/2 dias
Horas de contato – Aprox. 12 horas
Créditos – Equivalente a 1 crédito
Idioma – Português

FORMATO DE ENSINO
Este curso será um workshop prático. Para cada tópico, haverá uma apresentação estilo aula, isto é, utilizando slides ou lousa eletrônica, para introduzir conceitos-chaves e teoria. Então, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões.
No início de cada dia, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários.
Embora não seja estritamente necessário, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente.
Todas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha.
Todos os materiais, como slides, conjuntos de dados, etc., serão compartilhados via GitHub.

CONHECIMENTO QUANTITATIVO NECESSÁRIO
Um entendimento básico de conceitos estatísticos chaves. Especificamente, modelos de regressão linear, significância estatística e testes de hipóteses.

CONHECIMENTO COMPUTACIONAL NECESSÁRIO
Familiaridade com o R. Importar/exportar dados, manipular data frames, ajustar modelos estatísticos básicos e gerar gráficos simples.

REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE

Um computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC, Mac e Linux.
Participantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor, certifique-se de que você tem direitos de administrador em seu computador).
Um monitor grande e uma segunda tela, embora não seja absolutamente necessário, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

POR FAVOR, LEIA – POLÍTICA DE CANCELAMENTO
Cancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados, contate oliverhooker@prstatistics.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos, um reembolso completo das taxas do curso será creditado.

Se você estiver incerto em relação à adequabilidade do curso, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com

PROGRAMA DO CURSO

Terça-feira 20th
Aulas das 14:00 às 18:00 (Horário de Brasília)
DIA 1
Tópico 1: O modelo linear geral. Começamos com uma recapitulação do modelo normal, incluindo uso de variáveis preditoras. Embora esse modelo não seja o foco do curso, é o pilar central no qual os modelos lineares generalizados estão baseados e, portanto, deve ser compreendido para que haja entendimento dos modelos lineares generalizados (MLGs).
Tópico 2: Regressão logística binária. Nosso primeiro MLG é o de regressão logística binária (ou Bernoulli), a ser utilizado para modelar respostas binárias. Apresentaremos o modelo teórico por trás da regressão logística, implementaremos utilizando a função glm do R e mostraremos como interpretar os resultados, calcular predições e comparar modelos encaixados.
Tópico 3: Regressão logística binomial. Aqui, mostramos como a regressão logística para variáveis binarias pode ser estendida para lidar com dados que consistem de proporções discretas. Também apresentaremos funções de ligação alternativas ao logito, como a probito e complemento log-log.

Quarta-feira 21st
Aulas das 14:00 às 18:00 (Horário de Brasília)
Tópico 4: Regressão logística categórica. Também conhecida como regressão multinomial, é utilizada pra modelar dados politômicos, isto é, dados que assumem mais do que duas categorias distintas. Assim como a regressão logística ordinal, a regressão logística categórica também se baseia em uma extensão do caso de regressão logística binária.
Tópico 5: Regressão Poisson. A regressão Poisson é uma técnica amplamente utilizada para modelar dados de contagem, isto é, dados em que a variável resposta denota o número de vezes que um evento ocorreu.

Quinta-feira 22nd
Aulas das 14:00 às 18:00 (Horário de Brasília)
Tópico 6: Modelos de superdispersão. A abordagem de quase-verossimilhança para os modelos de Poisson e binomial. Regressão binomial negativa. O modelo binomial negativo é, assim como o modelo de regressão Poisson, utilizado para dados de contagem, mas é menos restritivo do que o modelo de Poisson, especificamente por lidar com dados superdispersos. Regressão beta-binomial. O modelo beta-binomial é uma alternativa ao modelo binomial que acomoda superdispersão.
Tópico 7: Modelos inflacionados de zeros. Dados de contagens inflacionados de zeros apresentam um número excessivo de contagens nulas quando modelados utilizando um modelo de Poisson on binomial negativo. Os modelos de Poisson ou binomial negativo inflacionados de zeros são exemplos de modelos de variáveis latentes.

Instrutor do curso

Dr. Rafael De Andrade Moral
Rafael é Professor Associado de Estatística na Maynooth University, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia, manejo da fauna silvestre, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui.
ResearchGateGoogleScholarORCIDGitHub

ONLINE COURSE – Data visualization with ggplot2 using R and Rstudio (DVGG04) This course will be delivered live
2024-02-20 - 2024-02-22 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, March 26th, 2023

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM
TIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
During this course we provide a comprehensive introduction to data visualization in R using ggplot. We begin by providing a brief overview of the general principles data visualization, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms, density plots, barplots, and Tukey boxplots. In all of these cases, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here, we will explore how to apply linear and nonlinear smoothing functions to the data, how to add marginal histograms to the scatterplot, add labels to points, and scale each point by the value of a third variable. We then cover some additional plot types that are often related but not identical to those major types covered during the beginning of the course: frequency polygons, area plots, line plots, uncertainty plots, violin plots, and geospatial mapping. We then consider more fine grained control of the plot by changing axis scales, axis labels, axis tick points, colour palettes, and ggplot “themes”. Finally, we consider how to make plots for presentations and publications. Here, we will introduce how to insert plots into documents using RMarkdown, and also how to create labelled grids of subplots of the kind seen in many published articles.

Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Information
Time zone – GMT+1
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format

This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.

Assumed quantitative knowledge
None needed.

Assumed computer background
Some familiarity with R.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 26th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 1
Topic 1: What is data visualization. Data visualization is a means to explore and understand our data and should be a major part of any data analysis. Here, we briefly discuss why data visualization is so important and what the major principles behind it are.
Topic 2: Introducing ggplot. Though there are many options for visualization in R, ggplot is simply the best. Here, we briefly introduce the major principles behind how ggplot works, namely how it is a layered grammar ofgraphics.
Wednesday 27th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 2

Topic 3: Visualizing univariate data. Here, we cover a set of major tools for visualizing distributions over single variables: histograms, density plots, barplots, Tukey boxplots. In each case, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots.
Topic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data. Here, in addition to covering how to visualize multiple groups using colours and facets, we will also cover how to provide marginal plots on the scatterplots, labels to points, and how to obtain linear and nonlinear smoothing of the plots.
Topic 5: More plot types. Having already covered the most widely used general purpose plots on Day 1, we now turn to cover a range of other major plot types: frequency polygons, area plots, line plots, uncertainty plots, violin plots, and geospatial mapping. Each of these are important and widely used types of plots, and knowing them will expand your repertoire.

Thursday 28th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Topic 6: Fine control of plots. Thus far, we will have mostly used the default for the plot styles and layouts. Here, we will introduce how to modify things like the limits and scales on the axes, the positions and nature of the axis ticks, the colour palettes that are used, and the different types of ggplot themes that are available.
Topic 7: Plots for publications and presentations: Thus far, we have primarily focused on data visualization as a means of interactively exploring data. Often, however, we also want to present our plots in, for example, published articles or in slide presentations. It is simple to save a plot in different file formats, and then insert them into a document. However, a much more efficient way of doing this is to use RMarkdown to run the R code and automatically insert the resulting figure into a, for example, Word document, pdf document, html page, etc. In addition, here we will also cover how to make labelled grids of subplots like those found in many scientific articles.

Course Instructor

Dr. Rafael De Andrade Moral

Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here

ResearchGateGoogleScholarORCIDGitHub

ONLINE COURSE - Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live
2024-01-29 - 2024-02-02 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, January 29th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata.
By the end of the course participants should:

Know the different types of spatial and spatio-temporal data available and how to work with them in R.
Know the different modelling approaches for spatial and spatio-temporal data.
Know how to visualize and produce maps of spatial and spatio-temporal data.
Be able to fit models with the R-INLA package.
Know how to interpret the output from model fitting.
Be confident with the use of INLA for data analysis.
Understand the different models that can be fit with INLA to spatial and spatio-temporal data.
Know how to define the different parts of a model with INLA.
Have the confidence to use INLA for their own projects.

Intended Audiences
Academics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.

Venue
Venue – Delivered remotely

Course Information
Time zone – Central European Standard Time (CEST)
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

Teaching Format

he course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference.
Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful.
Equipment and software requirements
A laptop/personal computer with any operating system (Linux, Windows, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
https://cran.r-project.org/
Download RStudio
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).

Assumed computer background
Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
oliverhooker@prstatistics.com

Course Programme

Monday 29th
Classes from 14:00 to 21:00 CET
DAY 1
LECTURE 1 – Intro to INLA
PRACTICAL 1 – Intro to INLA
LECTURE 2 – Model fitting with INLA
PRACTICAL 2 – Model fitting with INLA
LECTURE 3 – GLMM’s with INLA
PRACTICAL 3 – GLMM’s with INLA
Q and A and end of day summary

Tuesday 30th
Classes from 14:00 to 21:00 CET
DAY 2
LECTURE 4 – Spatial Data
PRACTICAL 4 – Spatial Data
LECTURE 5 – Spatio-Temporal Data
PRACTICAL 5 – Spatio-Temporal Data
LECTURE 6 – Advanced Visualisation
PRACTICAL 6 – Advanced Visualisation
Q and A and end of day summary

Wednesday 31st
Classes from 14:00 to 21:00 CET
DAY 3
LECTURE 7 – Spatial Models for Lattice Data
PRACTICAL 7 – Spatial Models for Lattice Data
LECTURE 8 – Spatial Models for Continuous Data
PRACTICAL 8 – Spatial Models for Continuous Data
LECTURE 9 – Spatial Models for Point Patterns
PRACTICAL 9 – Spatial Models for Point Patterns
Q and A and end of day summary

Thursday 1st
Classes from 14:00 to 21:00 CET
DAY 4
LECTURE 10 – Spatio-Temporal Models for Lattice Data
PRACTICAL 10 – Spatio-Temporal Models for Lattice Data
LECTURE 11 – Spatio-Temporal Models for Continuous Data
PRACTICAL 11 – Spatio-Temporal Models for Continuous Data
LECTURE 12 – Spatio-Temporal Models for Point Patterns
PRACTICAL 12 – Spatio-Temporal Models for Point Patterns
Q and A and end of day summary

Friday 2nd
Classes from 14:00 to 21:00 CET
DAY 5
Case studies, own data and problem solving.

Dr Virgillio Gomez Rubio

Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here

Researchgate
Google Scholar
ORCID
GitHub

ONLINE COURSE - Introduction to Machine Learning using R and Rstudio (IMLRPR)
2024-01-23 - 2024-01-25 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Course Format
Pre-Recorded

Course Details
In this three-day course, we provide a comprehensive practical and theoretical introduction to statistical machine learning using R. We start by introducing the concepts of supervised and unsupervised learning. We firstly explore unsupervised learning, and introduce k-means and
hierarchical clustering, as well as principal components analysis. We then move to supervised learning methods, and cover logistic regression and regularisation methods (such as ridge regression and the LASSO). After that, we introduce the k-nearest neighbours method, and classification and regression trees (CART). Finally, we explore extensions to CART, such as random forests and, if time allows, Bayesian additive regression trees (BART).

Intended Audiences
This course is aimed at anyone who is interested in statistical machine learning methods for clustering, classification or prediction, and using R fordata science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Information
Time zone – NA
Availability – NA
Duration – 3 1/2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format

This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R.

Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.

The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at: • 6pm-10pm

All sessions will be video recorded and made available to all attendees as soon as possible.

Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.

All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub

Assumed quantitative knowledge
A basic understanding of R and statistical concepts. Specifically, linear regression models, statistical significance, and hypothesis testing.

Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1
DAY 1
Section 1: Introductory concepts in statistical machine learning. Unsupervised vs. supervised learning. Useful plots in classification and clustering tasks. Unsupervised learning methods: hierarchical clustering and the k-means method.
Section 2: Dimension reduction techniques and principal components analysis.

Day 2
DAY 2
Section 3: Regression and classification tasks. Supervised learning methods: linear and logistic regression, regularisation methods (ridge, LASSO and elastic net).
Section 4: More supervised learning methods: smoothing methods, splines, and generalized additive models. Cross-validation techniques.

Day 3
DAY 3
Section 5: Tree-based methods. Classification and regression trees (CART), random forests.
Section 6: Extensions to tree-based methods. Bayesian additive regression trees (BART). Combining tree-based methods with a parametric regression framework.

Course Instructor

Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here
ResearchGate
GoogleScholar
ORCID
GitHub

ONLINE COURSE - Quantitative analysis of infrared spectroscopy data for soil and plant sciences (SPEC02) This course will be delivered live
2024-01-09 - 2024-01-11
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, January 9th, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

About This Course
This 3-day short course is aimed at providing an introduction to the analysis infrared spectroscopy data using the R programming language. Infrared spectroscopy is a high-throughput, non-destructive, and cheap sensing method that has a large range of applications in agricultural, plant and environmental sciences. Theory underpinning the visible, near and mid-infrared reflectance will be discussed, as well as interpretation of the wavelengths corresponding to specific molecular vibrations and the pre-processing of the raw spectra (day 1). We will then cover chemometric methods for exploratory spectral analysis with principal component analysis. We will have the opportunity to detect outlier spectra as well as to select the samples for laboratory analysis using the spectral data (day 2). Finally, we will introduce methods for building accurate multivariate models. Multivariate models will be explained and tested, including machine learning and conventional statistical algorithms. Sessions will be a blend of interactive demonstrations/practical and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, attendees will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:

Select the best pre-processing techniques for their own raw infrared spectral data.
Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis of infrared spectral data.
Select the optimal sample size and the best sampling design to subset spectral data and send the samples for laboratory analysis.
Understand and apply approaches for spectral data outlier detection.
Apply statistical multivariate modelling methods to infrared spectroscopy data and validate the model predictions.

Intended Audiences

This course is aimed at anyone who wishes to introduce into the analysis of visible, near and mid-infrared spectral data for plant and soil sciences. It is particularly suited for:

Graduate, post-graduate or post-doctoral level researchers who wish to learn how to analyse their own infrared data in R.
Applied researchers and analysts in the environmental or ecological sector with a role in handling and analysing infrared spectroscopy data.

Course Details
Time zone – CET

Availability – TBC
Duration – 3 days
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.

Assumed quantitative knowledge
Understanding of basic concept of sensing in the infrared range of the electromagnetic spectrum and prior knowledge of basic statistical techniques (e.g. linear regression).

Assumed computer background
Prior basic experience with performing statistical analyses using R and R Studio will be assumed, but is not a requirement.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open-source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve the learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 9th
Classes from 09:00 to 17:00 CET
DAY 1– Introduction to spectral inference in soil and plant sciences– Handling spectral data– Practical– The pre-processing of raw spectra– Practical– Exploratory spectral analysis– Practical

Wednesday 10th
Classes from 09:00 – 17:00 CET
DAY 2
– Spectral similarity analysis– The detection of outliers– Practical– Selecting the samples for laboratory analysis– Practical

Thursday 11th
Classes from 09:00 – 17:00 CET
DAY 3– Estimating properties from spectra -Multivariate statistical models– Practical– Validation of the predictions– Practical– Bring your own data! – OR large exercise estimating properties from raw spectra– Discussion & questions

Alexandre Wadoux

Alexandre Wadoux is a Research Associate in digital soil mapping at the University of Sydney and recently moved to the French National Institute for Agronomic and Environmental Research in Montpellier (France) to work on his Marie-Curie Fellowship. He has an undergraduate degree from the University of Angers in France, a MSc in soil science from the University of Tubingen in Germany, a Master in epistemology of sciences from the University of Nantes in France and a PhD in applied geostatistics from Wageningen University in the Netherlands. He has made contributions to several quantitative aspects of soil and environmental science through the development of methods for spatial sampling, mapping and assessment using geostatistics, statistical learning algorithms and spectroscopy. He is the author of the book “Soil Spectral Inference with R” published in 2021 with Springer.

ONLINE COURSE – Model selection and model simplification (MSMS04) This course will be delivered live
2024-01-09 - 2024-01-11 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Tuesday, January 9th, 2024

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM
TIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
​

About This Course
This three day course covers the important and general topics of statistical model building, model evaluation, model selection, model comparison, model simplification, and model averaging. These topics are vitally important to almost every type of statistical analysis, yet these topics are often poorly or incompletely understood. We begin by considering the fundamental issue of how to measure model fit and a model’s predictive performance, and discuss a wide range of other major model fit measurement concepts like likelihood, log likelihood, deviance, residual sums of squares etc. We then turn to nested model comparison, particularly in general and generalized linear models, and their mixed effects counterparts. We then consider the key concept of out-of-sample predictive performance, and discuss over-fitting or how excellent fits to the observed data can lead to very poor generalization performance. As part of this discussion of out-of-sample generalization, we introduce leave-one-out cross-validation and Akaike Information Criterion (AIC). We then cover general concepts and methods related to variable selection, including stepwise regression, ridge regression, Lasso, and elastic nets. Following this, we turn to model averaging, which is an arguably always preferable alternative to model selection. Finally, we cover Bayesian methods of model comparison. Here, we describe how Bayesian methods allow us to easily compare completely distinct statistical models using a common metric. We also describe how Bayesian methods allow us to fit all the candidate models of potential interest, including cases were traditional methods fail.

Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely.

Course Details
Time zone – GMT+1
Availability – TBC
Duration – 3 x 1/2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format

Assumed computer background

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Tuesday 9th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 1
Topic 1: Measuring model fit. In order to introduce the general topic of model evaluation, selection, comparison, etc., it is necessary to understand the fundamental issue of how we measure model fit. Here, the concept of conditional probability of the observed data, or of future data, is of vital importance. This is intimately related, though distinct, to concept of likelihood and the likelihood function, which is in turn related to the concept of the log likelihood or deviance of a model. Here, we also show how these concepts are related to concepts of residual sums of squares, root mean square error (rmse), and deviance residuals.
Topic 2: Nested model comparison. In this section, we cover how to do nested model comparison in general linear models, generalized linear models, and their mixed effects (multilevel) counterparts. First, we precisely define what is meant by a nested model. Then we show how nested model comparison can be accomplished in general linear models with F tests, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear models, and mixed effects models, we can accomplish nested model comparison using deviance based chi-square tests via Wilks’s theorem.

Wednesday 10th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 2
Topic 3: Out of sample predictive performance: cross validation and information criteria. In the previous sections, the focus was largely on how well a model fits or predicts the observed data. For reasons that will be discussed in this section, related to the concept of overfitting, this can be a misleading and possibly even meaningless means of model evaluation. Here, we describe how to measure out of sample predictive performance, which measures how well a model can generalize to new data. This is arguably the gold-standard for evaluating any statistical models. A practical means to measure out of sample predictive performance is cross-validation, especially leave-one-out cross-validation. Leave-one-out cross-validation can, in relatively simple models, be approximated by Akaike Information Criterion (AIC), which can be exceptionally simple to calculate. We will discuss how to interpret AIC values, and describe other related information criteria, some of which will be used in more detail in later sections.
Topic 4: Variable selection. Variable selection is a type of nested model comparison. It is also one of the most widely used model selection methods, and variable selection of some kind is almost always done routinely in all data analysis. Although we will also have discussed variable selection as part of Topic 2 above, we discuss the topic in more detail here. In particular, we cover stepwise regression (and its limitations), all subsets methods, ridge regression, Lasso, and elastic nets.

Thursday 11th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Topic 5: Model averaging. Rather than selecting one model from a set of candidates, it is arguably always better perform model averaging, using all the candidates models, weighted by the predictive performance. We show how to perform model average using information criteria.
Topic 6: Bayesian model comparison methods. Bayesian methods afford much greater flexibility and extensibility for model building than traditional methods. They also allow us to easily directly compare completely unrelated statistical models of the same data using information criteria such as WAIC and LOOIC. Here, we will also discuss how Bayesian methods allow us to fit all models of potential interest to us, including cases where model fitting is computationally intractable using traditional methods (e.g., where optimization convergence fails). This allows us therefore to consider all models of potential interest, rather than just focusing on a limited subset where the traditional fitting algorithms succeed.

Course Instructor

Dr. Rafael De Andrade Moral

Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here

ResearchGateGoogleScholarORCIDGitHub

ONLINE COURSE - Data wrangling using R and Rstudio (DWRS03) This course will be delivered live
2023-12-11 - 2023-12-14 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, December 11th, 2023

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
COURSE PROGRAM
TIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.

Course Details
During this course we provide a comprehensive practical introduction to data wrangling using R. In particular, we focus on tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly, it can be time consuming, laborious, and error-prone. Fortunately, the tools provided by R’s tidyverse allow us to do data wrangling in a fast, efficient, and high-level manner, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R, we then cover in detail all the dplyr tools such as select, filter, mutate, etc. Here, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here, we will consider how to concatenate data frames, including concatenating all data files in a folder, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider.

Intended Audiences
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Venue
Delivered remotely

Course Information
Time zone – GMT+1
Availability – TBC
Duration – 3 x 1/2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format

Assumed quantitative knowledge
Coming soon..

Assumed computer background
Minimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands, how to write code in the RStudio console and script editor, how to load up data from files, etc.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
oliverhooker@prstatistics.com

COURSE PROGRAMME

Classes from 12:00 to 16:00 (Central Time Zone)
DAY 1
Topic 1: Reading in data. We will begin by reading in data into R using tools such as readr and readxl. Almost all types of data can be read into R, and here we will consider many of the main types, such as csv, xlsx, sav, etc. Here, we will also consider how to contol how data are parsed, e.g., so that they are read as dates, numbers, strings, etc.
Topic 2: Wrangling with dplyr. For the remainder of Day 1, we will next cover the very powerful dplyr R package. This package supplies a number of so-called “verbs” — select, rename, slice, filter, mutate, arrange, etc. — each of which focuses on a key data manipulation tools, such as selecting or changing variables. All of these verbs can be chained together using “pipes” (represented by %>%). Together, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here, we will also learn about the key concept of “tidy data”, which is roughly where each row of a data frame is an observation and each column is a variable.
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 2
Topic 2 continued:
Topic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics.
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Topic 4: Merging and joining data frames. There are multiple ways to combine data frames, with the simplest being “bind” operations, which are effectively horizontal or vertical concatenations. Much more powerful are the SQL like “join” operations. Here, we will consider the inner_join, left_join, right_join, full_join operations. In this section, we will also consider how to use purrr to read in and automatically merge large sets of files.
Topic 5: Pivoting data. Sometimes we need to change data frames from “long” to “wide” formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this.

Course Instructor

Dr. Rafael De Andrade Moral

Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here

ResearchGateGoogleScholarORCIDGitHub

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ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIA01) This course will be delivered live
2023-12-04 - 2023-12-08 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, August 28th, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details
This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms, how they work, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts.
By the end of this course, students should be able to:

Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
Apply these techniques to real-world datasets and interpret the results.
Understand the underlying methods and assumptions/drawbacks of these techniques.

Intended Audiences
Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful.

Venue
Delivered remotely

Course Information
Availability – TBC
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors.

Assumed quantitative knowledge
A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests, Gradient Boosting, k-NN, SVMs.

Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 4th
Classes from 09:30 to 17:30 GMT+1
DAY 1
Deep Dive into Supervised Learning
We begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs), exploring their architecture, their use in image and video processing, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing.
In the afternoon sessions we implement CNNs and RNNs using real data sets
R Packages used: keras, tensorflow

Tuesday 5th
Classes from 09:30 to 17:30 GMT+1
DAY 2
Advanced Supervised Learning Techniques
On day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture, its self-attention mechanism, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks, and compare it to traditional machine learning methods.
In the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts.
R Packages: keras, tensorflow, rstan, brms, BART

Thursday 7th
Classes from 09:30 to 17:30 GMT+1
DAY 3
Unsupervised Learning – Clustering and Dimension Reduction
The third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering, DBSCAN, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions.
In the afternoon session students will explore the use of these techniques through real-world data sets.
R Packages: cluster, dbscan, factoextra, Rtsne, umap

Friday 8th
Classes from 09:30 to 17:30 GMT+1
DAY 4
Unsupervised Learning – Anomaly Detection and Course Wrap-up
On the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection, network security, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning.
In the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor.
R Packages: anomalize, forecast, e1071

Dr. Andrew Parnell

Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science, Nature Communications, and Proceedings of the National Academy of Sciences, and has methodological publications in journals such as Statistics and Computing, Journal of Computational and Graphical Statistics, The Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics, time series modelling, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change, 3D printing, and bioinformatics.
Research GateGoogle ScholarORCIDLinkedInGitHub

ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIAPR)
2023-12-04 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Course Format
Pre-Recorded

Course Details
This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms, how they work, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts.
By the end of this course, students should be able to:

Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
Apply these techniques to real-world datasets and interpret the results.
Understand the underlying methods and assumptions/drawbacks of these techniques.

Intended Audiences
Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful.

Venue
Delivered remotely

Course Information
Availability – TBC
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors.

Assumed quantitative knowledge
A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests, Gradient Boosting, k-NN, SVMs.

Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots.

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.

Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).

A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1
DAY 1
Deep Dive into Supervised Learning
We begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs), exploring their architecture, their use in image and video processing, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing.
In the afternoon sessions we implement CNNs and RNNs using real data sets
R Packages used: keras, tensorflow

Day 2
DAY 2
Advanced Supervised Learning Techniques
On day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture, its self-attention mechanism, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks, and compare it to traditional machine learning methods.
In the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts.
R Packages: keras, tensorflow, rstan, brms, BART

Day 3
DAY 3
Unsupervised Learning – Clustering and Dimension Reduction
The third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering, DBSCAN, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions.
In the afternoon session students will explore the use of these techniques through real-world data sets.
R Packages: cluster, dbscan, factoextra, Rtsne, umap

Day 4
DAY 4
Unsupervised Learning – Anomaly Detection and Course Wrap-up
On the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection, network security, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning.
In the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor.
R Packages: anomalize, forecast, e1071

Dr. Andrew Parnell

Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science, Nature Communications, and Proceedings of the National Academy of Sciences, and has methodological publications in journals such as Statistics and Computing, Journal of Computational and Graphical Statistics, The Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics, time series modelling, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change, 3D printing, and bioinformatics.
Research Gate
Google Scholar
ORCID
LinkedIn
GitHub

ONLINE COURSE – The Practice of RADseq: Population Genomics Analysis with Stacks (RADS02) This course will be delivered live
2023-10-02 - 2023-10-06 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, October 2nd, 2023

COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
This course is aimed at introducing researchers to the theory and practice of using reduced representation libraries – such as RAD sequencing – to preform population genomic analysis in non-model organisms. The course will center on running the software pipeline Stacks, focusing on how the characteristics of the underlying molecular libraries result in weak or robust analytical results. Sessions will be live online, consisting of a blend of lectures, interactive demonstrations, and lab practicals, where participants will have the opportunity to ask questions throughout. Computation will be done on the Amazon AWS Cloud.
By the end of the course, participants should be able to:

Navigate the UNIX file system, execute commands, and interact with bioinformatic data files;
Understand how to perform a de novo analysis – without a reference genome – including parameter optimization;
Understand how PCR duplicates and other molecular library characteristics affect analysis;
Complete a reference genome-based analysis;
Take the outputs from Stacks to complete a Structure analysis (de novo), a genome scan based on FST(reference-based), and a private allele analysis.

Intended Audiences
Graduate students, post-doctoral researchers, or professionals who wish to learn how to analyze genomic RAD-based data.

Venue
Delivered Remotely

Course Details
Availability – TBC
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Data and analytical approaches will be presented in a lecture format to introduce key concepts. In the beginning, participants will work interactively with the instructor to understand fundamentals. Once completed, the course will shift into a lab practical format, where the instructor introduces the lab, then free time is given for participants to complete the lab with the instructor present to answer questions. At the end of each practical, the instructor will go over the key ideas and results.

Assumed quantitative knowledge
Basic understanding of evolution (mutation, drift, selection, migration, HWE) and population-genomic concepts (e.g., FST, population structure) is assumed.

Assumed computer background
No computational background knowledge is assumed, however, experience in UNIX and/or bioinformatics analysis will enable participants to move at a faster pace.

Equipment and software requirements
Students will need a laptop or desktop with a fast and reliable internet connection. The computer can run any operating system including MacOS, Windows, or Linux, as we will connect, via the terminal, to our AWS instance on the Amazon Cloud.

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Day 1: 09:00 – 16:00 (Central Standard Time, i.e., Chicago)

Instructor and participant introductions
Lecture: Exploring the Genetics of Non-Model Organisms with RAD-seq
Introduction to the Cloud
Introduction to UNIX, Part 1

Day 2: 09:00 – 16:00

Short Lecture: Illumina error model, FastQ files, data quality control, and sample multiplexing
Cleaning and demultiplexing RAD-seq data
Introduction to UNIX, Part 2
Participant two-minute lightning talks

Day 3: 09:00 – 16:00

Short Lecture: Parameter optimization and de novo assembly of RAD tags
Understanding the de novo assembly algorithm
How to optimize assembly parameters in a de novo assembly
De novo assembly of RAD tags without a genome for a STRUCTURE Analysis

Day 4: 09:00 – 16:00

Referenced-aligned RAD tags for genome scanning and identifying signatures of selection
How to perform an integrated analysis – applying de novo data to a related reference genome

Day 5: 09:00 – 16:00

Short Lecture: Understanding DNA quality, molecular library integrity, and PCR duplicates
Examining the effects of PCR duplicates in a bird dataset
Performing a private allele analysis in a hybrid zone
Open lab, time for questions on participant provided data sets.

Course Instructor

Dr. Julian Catchen is an Associate Professor at the University of Illinois at Urbana-Champaign where he runs a population genomics lab that focuses on how the evolution of the genome affects underlying genomic architecture. He is the primary author of Stacks and has been involved in RADseq analysis since 2009, working on projects in a variety of fishes, birds, and insects while applying a diversity of genomic analyses including defining population structure, conducting genome scans, private allele analysis, and phylogenetics.
Lab Website: https://catchenlab.life.illinois.edu/Google Scholar: https://scholar.google.com/citations?user=YKnVJaAAAAAJ&hl=enResearch Gate: https://www.researchgate.net/profile/Julian-CatchenORCID: https://orcid.org/0000-0002-4798-660X

ONLINE COURSE - Advanced Ecological Niche Modelling (ENM/SDM) Using R (ANMR02) Deadline to register 28th August
2023-09-11 - 2023-09-15 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, September 11th, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Time Zone
TIME ZONE – UTC – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About This Course
Have you built an Ecological Niche Model? If yes, you have already encountered challenges on data preparation, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.
Ecological niche, species distribution, habitat distribution, or climatic envelope models are different names for mechanistic and correlative models, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental, topographical, human) to species physiological data or geographical locations, in order to identify the factors limiting and defining the species' niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management.
By the end of 5-day practical course you will have the capacity to

filter records and select your variables with variance inflation factor;
test the effect of Maxent regularization parameter in models performance;
validate models performance and accuracy;
perform MESS analysis, null models, and mechanistic models, as well as to build your “virtual species”.

Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Intended Audiences
This course is orientated to PhD and MSc students, as well as other students and researchers working on biogeography, spatial ecology, or related disciplines, with experience in ecological niche models.

Venue
Delivered remotely

Course Details
Availability – 24 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3ECT’s
Language – English

Teaching Format
The course will be mainly practical, with some theoretical lectures. All modelling processes and calculations will be performed with R, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.

Assumed quantitative knowledge
A basic understanding of ecological niche models and biogeography in general is required, thus we will assume the attendees know how to run an ecological niche model.

Assumed computer background
Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models, please consider attending our basic course on PRStatistics (www.prstats.org).

Equipment and software requirements

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

Download R

Download RStudio

Download Zoom

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com

COURSE PROGRAMME

Monday 11th
Classes from 09:30 to 17:30
Day One:

ENM guide: how to model
ENM R packages.
Sources of environmental variables using geodata package.
Getting species records with geodata package.

Tuesday 12th
Classes from 09:30 to 17:30
Day Two:

Variable selection with variance inflation factor (VIF) and usdm packages.
Choosing the correct study area.
Filtering records using usdm/spThin packages.
Choosing pseudo-absences with Biomod2 package.

Wednesday 13th
Classes from 09:30 to 17:30
Day Three:

Split records in training and test with ENMeval package.
Test effect of Maxent regularization parameter.
Comparing correlative models with AIC, with ENMeval package.

Thursday 14th
Classes from 09:30 to 17:30
Day Four:
 MESS practice with Biomod2 package.
 Validate models null models.
 VirtualSpecies virtualspecies packages.

Friday 15th
Classes from 09:30 to 17:30
Day Five:

Mechanistic model NicheMapper packages.

Dr. Neftali Sillero

Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns, from species to populations and individuals. For this, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems, Remote Sensing, Ecological Niche Modelling, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases, ecological modelling of species’ ranges, identification of biogeographical regions and species’ chorotypes, mapping and modelling road-kill hotspots, and spatial analyses of home ranges.
He has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea, where he is the PI of the NA2RE project (www.na2re.ismai.pt), the New Atlas of Amphibians and Reptiles of Europe
Personal website
Work Webpage
ResearchGate
GoogleScholar

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Teaches

Ecological Niche Modelling Using R (ENMR)
Advanced Ecological Niche Modelling Using R (ANMR)
GIS And Remote Sensing Analyses With R (GARM)

Dr. Salvador Arenas-Castro

Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology, forestry, spatial ecology, andecoinformatics, all addressed by explicitly considering the spatial component ofecological processes, mainly applying spatially explicit modelling approaches, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com
Google Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro

ONLINE COURSE - Introduction to Bayesian modelling with INLA (BMIN02) This course will be delivered live
2023-05-22 - 2023-05-26 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Hidden Markov Models for movement, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live

Event Date
Monday, May 22nd, 2023

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Details
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics on the INLA methodology as well as practical modelling of different types of data.
By the end of the course participants should:

Understand the basics of Bayesian inference.
Understand how the INLA method works and its main differences with MCMC methods.
Be able to fit models with the R-INLA package.
Know how to interpret the output from model fitting.
Be confident with the use of INLA for data analysis.
Understand the different models that can be fit with INLA.
Know how to define the different parts of a model with INLA.
Be able to develop new latent effects not implemented in the R-INLA package.
Know how to define new priors not included in the R-INLA package.
Have the confidence to use INLA for their own projects.

Intended Audiences
Academics and post-graduate students working on projects related to data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.

Venue
Venue – Delivered remotely

Course Information
Time zone – Central European Standard Time (CEST)
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

Teaching Format

he course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference.
Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful.
Equipment and software requirements
A laptop/personal computer with any operating system (Linux, Windows, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
https://cran.r-project.org/

Download RStudio

UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

Assumed quantitative knowledge
Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).

Assumed computer background
Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed.

Equipment and software requirements
Attendees will need to install/update R/RStudio and various additional R packages.
This can be done on Macs, Windows, and Linux.
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.

Tickets are no longer available

PLEASE READ – CANCELLATION POLICY
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

If you are unsure about course suitability, please get in touch by email to find out more
info@clovertraining.co.uk

Monday 22nd
Classes from 14:00 to 21:00
Introduction to the courseKey concepts related to Bayesian inferenceModels with conjugate priorsIntroduction to Bayesian hierarchical modelsComputational methods for Bayesian inferenceIntroduction to the INLA methodologyFitting generalized linear models with INLA and the R-INLA packageUnderstanding and manipulating the output from model fitting with R-INLA

Tuesday 23rd
Classes from 10:00 – 17:00Fitting generalized linear mixed models with R-INLATypes of latent effects in R-INLAModels with i.i.d. latent effectsFitting multilevel models with R-INLAModels with correlated latent effectsFitting time series models with R-INLA

Wednesday 24th
Classes from 14:00 – 21:00Priors in R-INLASetting priors in R-INLAIntroduction to Penalized Complexity priors (PC-priors)Defining new priors in R-INLASpatially correlated random effectsFitting spatial models with R-INLAVisualizing the output from spatial models and mapping

Thursday 25th
Classes from 14:00 – 21:00Advanced features in R-INLAComputing linear combinations of the latent effectsFitting models with several likelihoodsModels with shared termsAdding linear constraints to the latent effectsImplementing new latent models in R-INLAImputation and missing covariates in R-INLA

Friday 26th
Classes from 14:00 to 21:00
Case studies and own data.

Course Instructor

Dr Virgillio Gomez Rubio
Works at: Universdad de Castilla~La Mancha
Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference withINLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on his website.

Researchgate: https://www.researchgate.net/profile/Virgilio-Gomez-Rubio?ev=hdr_xprf
Google Scholar: https://scholar.google.es/citations?user=OggVQCkAAAAJ&hl=es
ORCID: https://orcid.org/0000-0002-4791-3072
GitHub: https://github.com/becarioprecario
Homepage: https://becarioprecario.github.io
