PR Statistics

ONLINE COURSE - Path analysis, structural equations and causal inference for biologists (PSCB03) This course will be delivered live
2025-10-20 - 2025-10-30 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, October 20th, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructors 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 – Eastern Daylight 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 course, based primarily on my 2016 book, teaches you how to use path analysis and structural equations modelling to test causal hypotheses using observational data that is typical of research in ecology and evolution. It is taught in half-day sessions so that you can practice individually after each half-day session. You will learn how to conduct these tests, why (andwhen) they are justified, and how to interpret the results. The first few lectures will primarily present the theory but practical sessions will become more prominent later in the course. Thepractical work will be based on R and RStudio. Students will receive R script, datasets, and a list of R packages to install. It is highly recommended that each student have a copy of my 2016 book for the course, but not essential.
By the end of the course, participants should be able to: Understand the logical relationships between d-separation, data, and causal hypotheses. Know when to use piecewise SEM, when to use covariance- based SEM, and the advantages/disadvantages and assumptions of each Be able to construct, test, and interpret measurement models involving latent variables Be able to construct and identify equivalent models Be able to incorporate nested or mixed models, multigroup models, and non-normal distributions into SEM
Participants are encouraged to bring their own data, as there will be opportunities throughout the course to plan, analyze, and receive feedback on structural equation models.

Intended Audiences
Scientists generally, and ecologists specifically, who want to test hypotheses concerning cause-and-effect relationships involving several variables, especially involving observational data.

Venue
Delivered remotely

Course Details
Availability – TBC
Duration – 7 x 1/2 days
Contact hours – Approx. 25 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
This course involves a mixture of theory and practical work. Data and analytical approaches will be presented in a lecture format to explain 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 quantative knowledge
Experience in using R and RStudio for statistical analysis. A basic understanding of statistical inference and regression methods. A familiarity of more advanced regression models (mixed models, generalized linear models) is an asset but is not essential.

Assumed computer background
Proficiency with R programming language, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g., lm()).

Equipment and software requirements
A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. A full list of required packages will be made available to participants prior to the course.
https://cran.r-project.org/Download RStudio
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com

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
Day 1 08:30 – 12:00Causal inference using experiments vs. observations (1h) Randomised experiments are the gold standard Limitations on randomised experiments The logic of controlled experiments Limitations of controlled experiments Physical control vs. observational control DAGs, d-separation and data (2h) Translating from the language of causality to the language of statistics Directed acyclic graphs (DAGS) and d-separation D-separation and statistical conditioning The difference between experimental control and statistical conditioning The logic of causal inference using d-separation

Tuesday 21st
Day 2 08:30 – 12:00Path analysis using piecewise structural equation modelling (1h30) D-separation basis sets of a DAG The steps in conducting a piecewise SEM Rejecting or provisionally accepting your path model Path coefficients as measures of direct causal effect Decomposing causal effects
Practical work (2h)

Wednesday 22nd
Day 3 8:30 – 12:00Path analysis using piecewiseSEM (2h30) The piecewiseSEM library in R
Practical work (1h)

Thursday 23rd
Day 4 08:30 – 12:00Equivalent models and AIC statistics (2h) Statistical power in SEM Provisionally accepting a causal hypothesis What is a “d-separation equivalent” DAG Rules for identifying equivalent models AIC statistic to compare between non-equivalent models How to interpret AIC statistics
Practical work (1h30)

Friday 24th
Day 5 08:30 – 12:00Covariance-based path analysis (2h) Translating the DAG into “structural equations” The model-predicted covariance matrix An intuitive explanation of maximum likelihood estimation Estimating the free parameters via ML The concept of “degrees of freedom” The ML chi-squared statistic of model fit Rejecting (or not) your SE model
Covariance-based path analysis using lavaan (1h30)

Monday 27th
Day 6 08:30 – 11:30
Latent variables and measurement models (3h) Removing latent variables from a DAG DAGs and MAGs DAG.to.MAG() function When you can’t remove a latent: measurement models Measurement models and ML estimation Fixing the scale of a latent variable Measurement models and minimum degrees of freedom Measurement models in lavaan Empirical example: measuring soil fertility

Tuesday 28th
Day 7 08:30 – 12:00
Practical using measurement models (1h)
The full structural equation model (2h30) Model identification: structural and empirical Composite variables and composite latents Consequences and solutions for small sample sizes Consequences and solutions for non-normal data Measures of approximate fit Missing data Reporting results in publications

Wednesday 29th
Day 8 08:30 – 12:00Multigroup models (2h) What is causal heterogeneity? The concept of nested models How to fit multigroup models in lavaan
Practical: putting everything together (1h30)

Thursday 30th
Day 9 9:00 – 12:00Practical and group presentations of results

Bill Shipley

Bill Shipley is an experienced researcher and
teacher in plant ecology and statistical ecology. He has published four scientific monographs and over 170 peer-reviewed papers.

ONLINE COURSE - Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live
2025-10-06 - 2025-10-10 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, October 6th, 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.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
Time Zone
TIME ZONE – GMT – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

About this course
The study of animal acoustic signals is a central tool for many fields in behavior, ecology, evolution and biodiversity monitoring. The accessibility of recording equipment and growing availability of open-access acoustic libraries provide an unprecedented opportunity to study animal acoustic signals at large temporal, geographic and taxonomic scales. However, the diversity of analytical methods and the multidimensionality of these signals posts significant challenges to conduct analyses that can quantify biologically meaningful variation. The recent development of acoustic analysis tools in the R programming environment provides a powerful means for overcoming these challenges, facilitating the gathering and organization of large acoustic data sets and the use of more elaborated analyses that better fit the studied acoustic signals and associated biological questions. The course will introduce students on the basic concepts in animal acoustic signal research as well as hands-on experience on analytical tools in R.
By the end of the course, participants should:

Understand the basic concepts of bioacoustics and how animal acoustic signals are analyzed
Gain proficiency in handling and manipulating acoustic data in R, including working with ‘wave’ objects and other audio formats
Develop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R
Import Raven Pro annotations into R and refine these annotations with warbleR functions
Understand how to quantify the structure of acoustic signals through various approaches
Gain experience in quality control of recordings and annotations, ensuring data integrity and accuracy
Compare different methods for quantifying acoustic signal structure and understand the implications of each approach

Intended Audiences

Academics and post-graduate students conducting research in bioacoustics, animal behavior, ecology, or related fields
Applied researchers and analysts in public, private, or non-profit organizations who require robust, reproducible, and flexible tools for analyzing acoustic data
Current R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis
Wildlife biologists, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies
Data scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals

Venue
Delivered remotely

Course Details
Time Zone – GMT
Availability – 20 places
Duration – 5 days, 4 hours per day
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

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. 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 6th
Day 1 – Classes from 13:30 – 17:30Introduction
– How animal acoustic signals look like?An overview of the variety of acoustic signals produced by animals, with examples from differentspecies. This includes visualizing sound waves and spectrograms to understand their structureand complexity.
– Analytical workflow in bioacoustics researchIntroduction to the step-by-step process involved in bioacoustic research, from recording anddata collection to analysis and interpretation. This session will outline the typical workflow,emphasizing the importance of each step.
– Advantages of programmingDiscussion on the benefits of using programming languages like R for bioacoustic analysis,including reproducibility, efficiency, and the ability to handle large datasets. This will highlighthow programming can enhance research capabilities.
What is sound?– Sound as a time seriesExplanation of how sound can be represented as a time series, with each point in the seriesrepresenting the sound pressure level at a given moment in time. This forms the basis for furtheranalysis and manipulation.
– Sound as a digital objectDiscussion on the digitization of sound, including sampling rates, bit depth, and the conversion ofanalog sound waves into digital formats that can be analyzed using software.
– Acoustic data in RIntroduction to handling and analyzing acoustic data in R. This includes importing sound files,basic data exploration, and visualization techniques.
– ‘wave’ object structureExplanation of the ‘wave’ object in R, its structure, and the information it contains. This isessential for understanding how to manipulate and analyze sound data in R.
– ‘wave’ object manipulationsTechniques for manipulating ‘wave’ objects, including trimming, concatenating, and modifyingsound files. Practical exercises will be provided to reinforce these concepts.
– Additional formatsOverview of other audio file formats (e.g., MP3, FLAC) and how they can be converted and used inR for bioacoustic analysis.

Tuesday 7th
Day 2 – Classes from 13:30 – 17:30
Building spectrograms– Fourier transformExplanation of the Fourier transform and its application in converting time-domain signals intofrequency-domain representations. This is the foundation for creating spectrograms.
– Building a spectrogramStep-by-step guide on how to construct spectrograms, including parameter selection (e.g.,window size, overlap) and interpretation of the resulting visual representations.
– Characteristics and limitationsDiscussion on the strengths and limitations of spectrograms, including resolution trade-offs andpotential artifacts. Participants will learn to critically evaluate spectrograms.
– Spectrograms in RPractical session on generating and customizing spectrograms in R using the seewave package.Participants will create spectrograms from their own data.Package seewave
– Explore, modify and measure ‘wave’ objectsHands-on exploration of the seewave package, focusing on functions for modifying andmeasuring 'wave' objects. This includes exercises on filtering, re-sampling, and extracting acousticfeatures.
– Spectrograms and oscillogramsCreating and interpreting both spectrograms and oscillograms in R. Participants will learn tovisualize sound data in different ways to highlight various aspects of the signal.
– Filtering and re-samplingTechniques for filtering (e.g., band-pass, high-pass) and re-sampling sound files to focus onspecific frequency ranges or standardize sampling rates.
– Acoustic measurementsUsing the seewave package to perform detailed acoustic measurements, such as peak frequency,dominant frequency, and frequency range. Practical examples will be provided.

Wednesday 8th
Day 3 – Classes from 13:30 – 17:30
Annotations– Introduction to the Raven Pro InterfaceA guided tour of the Raven Pro software, its main features, and interface elements. Participantswill learn how to navigate the software efficiently.
– Introduction to selections and measurementsInstruction on how to make selections within sound files and take basic measurements such asduration and frequency using Raven Pro.
– Saving, retrieving, and exporting selection tablesHow to save, retrieve, and export selection tables in Raven Pro for further analysis. This sessionwill cover best practices for data management and organization.
– Using annotationsTechniques for annotating sound files in Raven Pro, including the use of labels and notes to marksignificant events or features within the recordings.
Quantifying acoustic signal structure– Spectro-temporal measurements (spectro_analysis())Introduction to the spectro_analysis() function in R for extracting spectro-temporalmeasurements from audio recordings. Participants will learn to describe acoustic signals in termsof their temporal and spectral characteristics.
– Parameter descriptionDetailed explanation of key acoustic parameters, such as duration, frequency range, andamplitude, and how they are used to describe sound signals.
– Harmonic contentTechniques for analyzing the harmonic content of signals, including identifying harmonic seriesand measuring harmonic-to-noise ratios.
– Cepstral coefficients (mfcc_stats())Introduction to Mel-frequency cepstral coefficients (MFCCs) and their use in characterizing thetimbral properties of sound signals. Participants will use the mfcc_stats() function to extractMFCCs.
– Cross-correlation (cross_correlation())Explanation of cross-correlation techniques for comparing sound signals. Participants will usecross_correlation() to measure the similarity between different recordings.
– Dynamic time warping (freq_DTW())Introduction to dynamic time warping (DTW) and its application in aligning and comparing time-series data. The freq_DTW() function will be used to compare sound signals.
– Signal-to-noise ratio (sig2noise())Techniques for calculating the signal-to-noise ratio (SNR) of recordings, which is crucial forassessing the quality of sound data.
– Inflections (inflections())Identifying and measuring inflections in sound signals, which can indicate changes in pitch orother dynamic features.
– Parameters at other levels (song_analysis())Exploring acoustic parameters at higher hierarchical levels, such as entire songs or sequences ofvocalizations, using the song_analysis() function.

Thursday 9th
Day 4 – Classes from 13:30 – 17:30
Quality control in recordings and annotations– Create catalogsCompiling catalogs of annotated sound files, which can be used for further analysis or asreference materials.
– Check and modify sound file format (check_wavs(), info_wavs(), duration_wavs(),mp32wav() y fix_wavs())Techniques for checking and modifying sound file formats using various functions in R. Thisincludes converting files, checking file integrity, and fixing common issues.
– Tuning spectrogram parameters (tweak_spectro())Adjusting spectrogram parameters to optimize the visualization and analysis of sound signals.Participants will use tweak_spectro() to fine-tune their spectrograms.
– Double-checking selection tables (check_sels(), spectrograms(), full_spectrograms() &catalog())Methods for verifying and refining selection tables, ensuring that all annotations are accurate andcomprehensive.
– Re-adjusting selections (tailor_sels())Techniques for re-adjusting selections in response to quality control checks, ensuring that allannotations are precise and correctly positioned.Characterizing hierarchical levels in acoustic signals
– Creating ‘song’ spectrograms (full_spectrograms(), spectrograms())Building spectrograms that represent entire songs or sequences of vocalizations, providing ahigher-level view of acoustic patterns.
– ‘Song’ parameters (song_analysis())Measuring and analyzing parameters at the song level, such as song duration, number ofelements and element rate, using the song_analysis() function.

Friday 10th
Day 5 – Classes from 13:30 – 17:30
Choosing the right method for quantifying structure– Compare different methods for quantifying structure (compare_methods())Comparing various methods for quantifying acoustic signal structure. Participants will usecompare_methods() to evaluate different approaches.Quantifying acoustic spaces
– Intro to PhenotypeSpaceIntroduction to the concept of acoustic spaces and the PhenotypeSpace framework, which allowsfor the visualization and comparison of acoustic diversity.
– Quantifying space sizeTechniques for measuring the size of acoustic spaces, which can provide insights into thevariability and complexity of vocalizations.
– Comparing sub-spacesMethods for comparing different sub-spaces within the overall acoustic space, allowing for theanalysis of variations between species, populations, or other groups.
Each of these topics will be covered with detailed explanations, practical examples, and hands-onexercises to ensure that participants gain a comprehensive understanding of bioacoustics researchusing the R platform.

Course Instructor

*
Dr. Marcelo Araya Salas
Works at – Neuroscience Research Center, Universidad de Costa Rica
Marcelo Araya-Salas works at the intersection of scientific programming and evolutionary behavioral ecology, focusing on the evolution of behavior and the factors influencing it across cultural and evolutionary timescales. His research primarily examines the communication systems of neotropical species using single-species behavioral studies, comparative phylogenetic methods, and advanced data analysis techniques. He has developed several computational tools for biological data analysis, including the R packages warbleR, Rraven and baRulho which simplify the manipulation of annotated acoustic data and the quantification of structure and degradation of animal sounds.
ResearchGate
Google Scholar
Work Homepage
Personal Homepage

ONLINE COURSE - Tidyverse for Ecologists (TIDY01) This course will be delivered live
2025-06-16 - 2025-06-20 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, June 16th, 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 the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming, core Tidyverse packages, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology. By the end of the course, participants should: Understand the fundamentals of R programming for data analysis. Be proficient in using core Tidyverse packages to clean, transform, and visualise data. Gain an introduction to basic machine learning concepts through the Tidymodels framework. Learn to preprocess, build, evaluate, and interpret models using Tidymodels. Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects.

Intended Audiences

Academics and post-graduate students working on data science-related projects.
Data scientists and applied researchers in public or private sectors who need to integrateadvanced R programming language into their project workflows.
Professionals looking to integrate tidyverse packages into their workflows or enhance theirunderstanding of R programming language.
Ecologists looking to understand the basic principles of advanced R programming language and implement them in their research.

Venue
Delivered remotely

Course Information
Time zone – Ireland local time
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
No quantitative knowledge is required for this module.

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

Equipment and software requirements

A computer with a working version of R or RStudio is required. R and RStudio are 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 computer administration rights).
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.
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

Monday 16th
Day 1: A Short Course in R Basics (9:30 – 17:30)
This day provides participants with the foundational R skills required for working with Tidyverse andTidymodels. It is designed for beginners or those needing a refresher in R programming.
 Section 1 (R Essentials): This section focuses on R 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 R): This section emphasises understanding data structures (e.g., vectors, data frames, lists) and handling files by reading/writing data (e.g., CSVs) for manipulation and analysis.

Tuesday 17th
Day 2: Fundamentals of Tidyverse I (9:30 – 17:30)
This day introduces participants to the foundational concepts of Tidyverse packages and theirapplications to data science projects.
 Section 3 (Data Manipulation I): This section covers the basics of data manipulation using `dplyr` functions such as `filter()`, `select()`, `mutate()`, `arrange()`, and `summarise ()`. Participants will learn how to clean, transform, and prepare datasets for analysis. Section 4 (Data Visualisation I): This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots, bar charts, and line graphs while exploring the grammar of graphics.

Wednesday 18th
Day 3: Fundamentals of Tidyverse II (9:30 – 17:30)
This day builds on the foundations established in Day 2 and dives deeper into advanced datamanipulation and visualisation techniques.
 Section 5 (Data Manipulation II): This section extends the use of `dplyr` by introducing morecomplex operations such as joins, grouping with `group_by()`, and working with pipelines using`%>%`. Finally, additional packages will be presented to enhance data manipulationprogramming. Section 6 (Data Visualisation II): Participants will explore advanced visualisation techniquesusing extensions of `ggplot2`, such as creating animated plots with the `gganimate` package andinteractive visualisations with additional tools.

Thursday 19th
Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 – 17:30)
This day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning.
 Section 7 (Introduction to regression): This section focuses on regression modelling usingTidymodels. Participants will learn to implement linear regression models, evaluate modelperformance, and interpret results. Section 8 (Introduction to Classification): This section introduces techniques such as supportvector machines and neural networks using Tidymodels. Participants will also explore methodsfor assessing the performance of classification models.

Friday 20th
Day 5: Data Science Workflow with Tidyverse (9:30 – 17:30)
On the final day, participants will apply all their newly acquired skills to solve real-world problemsinspired by ecological datasets.
 Section 9 (The data science workflow): The workflow will be illustrated based on the corepackages introduced. The book "R for Data Science" will serve as a base literature for this day Section 10 (Hands-on project): Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results.

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
ResearchGate
GoogleScholar

ONLINE COURSE - Movement Ecology Using R(MOVE07) This course will be delivered live
2025-05-12 - 2025-05-16 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, May 12th, 2024

Course Format
This is a ‘LIVE COURSE’ – the instructors 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+1 – 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 course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts.

Intended Audiences
Research postgraduates, practicing academics and primary investigators in ecology and management and environmental professionals in government and industry. The course will also be of interest to researchers in geography, mathematics and computer science working on movement analyses.

Venue
Delivered remotely

Course Details
Availability – TBC
Duration – 5 days
Contact hours – Approx. 37 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
A basic understanding of statistical, mathematical and physical concepts. Specifically, generalised linear regression models, including mixed models; basic knowledge of trigonometry, basic knowledge of calculus; basic knowledge of physics as relevant for biological systems.

Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models (up to GLM); generate simple exploratory and diagnostic plots. Knowledge of more advanced models, such as mixed models, will be helpful, as will a basic recollection of mathematical analysis.

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 12th
Classes from 12:00 to 20:30 GMT+1
DAY 1Movement FundamentalsConceptual component: Introduction to movement ecology, movement and behaviour, spatial and movement path analysis.Practical component: Movement path analysis I – from steps and turns to movement path segmentation; Movement path analysis II – movement modes (home rage, dispersal, migration, nomadism) and the squared displacement method.
Attendees will be guided through practicals by both instructors to ensure you get the most from their experience.

Tuesday 13th
Classes from 12:00 – 20:30 GMT+1
DAY 2Home Range AnalysisConceptual component: Ecological definitions and interpretations of home ranges, home range estimation, comparisons between estimators and the question-driven approach.Practical component: Utilization distribution; comparison of contrasting kernel home range estimation methods, isopleth creation, core area & home range overlap.
Attendees will be guided through practicals by both instructors to ensure you get the most from their experience.

Wednesday 14th
Classes from 12:00 – 20:30 GMT+1
DAY 3Dynamic Interactions and Temporal Metrics of MovementConceptual component: Movements of interacting animals – static and dynamic interactions; scales of movement – first-passage and residence time analysis.Practical component: Static and dynamic interaction indices; estimation of first-passage and residence time metrics.
Attendees will be guided through practicals by both instructors to ensure you get the most from their experience.

Thursday 15th
Classes from 12:00 – 20:30 GMT+1
DAY 4Introduction to Resource Selection, and Effects of ScaleConceptual component: Theories of resource and habitat selection, history of approaches, and current methodologies and caveats including definitions of availability and scale effects for RSF and other movement metricsPractical component: Data projections and R as a GIS; Scale-integrated models of movement, availability sampling, and RSF estimation and interpretation.
Attendees will be guided through practicals by both instructors to ensure you get the most from their experience.

Friday 16th
Classes from 12:00 – 20:30 GMT+1
DAY 5Step-Selection Functions and Instantaneous AvailabilityConceptual component: Introduction to step selection, decision-making processes, null and alternative models for definitions of availability within SSF, movement-integrated step-selection analysisPractical component: Creation of available step data, estimation of SSF using multiple packages and approaches, simulation of utilization and occurrence distributions.
Attendees will be guided through practicals by both instructors to ensure you get the most from their experience.

Course Instructor

Prof. Luca Borger
Coming Soon

Course Instructor

Dr. Garrett Street
Coming Soon

ONLINE COURSE - Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live
2025-05-12 - 2025-05-14
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, May 12th, 2025

Course Format
Pre Recorded

About This Course
In this three-day course, we introduce species distribution models (SDMs) and ways toincorporate phylogenetic information into single species models using R. We begin byproviding an overview on the use of SDMs as a central tool for ecologists and evolutionarybiologists, review and implement common SDM approaches and introduce hybrid models,which use the information in functional traits to complement the models. We then justifythe rationale for using phylogenetic information in absence of functional trait data andshow how to incorporate phylogenetic information in SDMs (day 1). We review examplesof practical implementation of PSDMs to both present and future climate scenarios (day 2).Finally, we overview more advanced approaches of incorporating phylogenies into models(the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatialcontext (day 3).

Intended Audiences
This course is aimed at anyone who wishes to improve/complement their use of SpeciesDistribution Models using phylogenies.

Course Details
Venue – Delivered remotelyAvailability – 20 placesDuration – 3 daysContact hours – Approx. 18 hoursECT’s – Equal to 2 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.
The course will take place online using Zoom. On each day, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:00pm-16:00pm
All sessions will be video recorded and made available to all attendees.
Attendees in different time zones will be able to join into some of these live broadcasts, even if all of them are not convenient times.
By joining any live sessions that are possible, this will allow attendees to benefit Fromm asking questions and having discussions, rather than just watching prerecorded sessions.
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. summarystatistics, distributions).

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 inR (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

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/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

Monday 12th
Classes from 8:00 to 16:00
Introduction to species distribution models: rationale, algorithms, validation and applications.
• Working with SDMs. Implementing SDMs in R.• Hybrid-SDMs or how to incorporate functional information into the models.• What to do in absence of functional trait data? The rationale for using the latent information in phylogenies instead.• The phylogenetic predictor.

Tuesday 13th
Classes form 08:00 – 16:00
Putting phylogenies in the geography: how to combine phylogenies with speciesdistribution models in R.
• Phylogenetic information can improve both present and future predictions ofspecies distributions.• Projecting phyloSDMs across space and time in R.• When and why phylogenies can and can’t improve models.

Wednesday 14th
Classes form 08:00 – 16:00
Phylogenies also improve models for the temporal distribution of species.

The Bayesian Phylogenetic Mixed Model
Examples of implementation of PMMs and extrapolating their predictions to thegeography in R.

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

Movement Ecology (MOVEPR)
2025-05-05 - 2025-05-09 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

COURSE FORMAT
Pre Recorded

About This Course
The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts.

Intended Audiences
Research postgraduates, practicing academics and primary investigators in ecology and management and environmental professionals in government and industry. The course will also be of interest to researchers in geography, mathematics and computer science working on movement analyses.

Course Details
Last Up-Dated – 17:03:2023
Duration – Approx. 40 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

Assumed quantitative knowledge
A basic understanding of statistical, mathematical and physical concepts. Specifically, generalised linear regression models, including mixed models; basic knowledge of trigonometry, basic knowledge of calculus; basic knowledge of physics as relevant for biological systems.

Assumed computer background
Good familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models (up to GLM); generate simple exploratory and diagnostic plots. Knowledge of more advanced models, such as mixed models, will be helpful, as will a basic recollection of mathematical analysis.

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. 8 hours
Movement FundamentalsConceptual component: Introduction to movement ecology, movement and behaviour, spatial and movement path analysis.Practical component: Movement path analysis I – from steps and turns to movement path segmentation; Movement path analysis II – movement modes (home rage, dispersal, migration, nomadism) and the squared displacement method.
Day 2 – approx. 8 hours
Home Range AnalysisConceptual component: Ecological definitions and interpretations of home ranges, home range estimation, comparisons between estimators and the question-driven approach.Practical component: Utilization distribution; comparison of contrasting kernel home range estimation methods, isopleth creation, core area & home range overlap.
Day 3 – approx. 8 hours
Dynamic Interactions and Temporal Metrics of MovementConceptual component: Movements of interacting animals – static and dynamic interactions; scales of movement – first-passage and residence time analysis.Practical component: Static and dynamic interaction indices; estimation of first-passage and residence time metrics
Day 4 – approx. 8 hours
Introduction to Resource Selection, and Effects of ScaleConceptual component: Theories of resource and habitat selection, history of approaches, and current methodologies and caveats including definitions of availability and scale effects for RSF and other movement metricsPractical component: Data projections and R as a GIS; Scale-integrated models of movement, availability sampling, and RSF estimation and interpretation
Day 5 – approx. 8 hours
Step-Selection Functions and Instantaneous AvailabilityConceptual component: Introduction to step selection, decision-making processes, null and alternative models for definitions of availability within SSF, movement-integrated step-selection analysisPractical component: Creation of available step data, estimation of SSF using multiple packages and approaches, simulation of utilization and occurrence distributions.

Course Instructor

Prof. Luca Borger
Teaches:

Movement Ecology (MOVE)

Personal website
Work Webpage
ResearchGate
GoogleScholar
Luc Borger…
Course Instructor

Dr. Jarrett Street
Teaches:

Movement Ecology (MOVE

Personal website
Work Webpage
ResearchGate
GoogleScholar
Garrett Street…

Model-Based Multivariate Analysis Of Abundance Data Using R (MBMVPR)
2025-05-05 - 2025-05-16 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data. Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework. In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and use of species traits as predictors to explain interspecific variation in environmental response. These techniques are more interpretable than alternatives, have better statistical properties, and can be used to address new problems, such as the prediction of a species’ spatial distribution from its traits alone.

Intended Audiences
PhD students, research postgraduates, and practicing academics as well as persons in industry working with multivariate data, especially when recorded as presence/absences or some measure of abundance (counts, biomass, % cover, etc).

Course Details
Last Up-Dated – 12:02:2021
Duration – Approx. 30 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
A mixture of lectures and hands-on practical’s. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

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

Assumed computer background
Previous experience with data analysis using R is required. 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. 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. 3 hoursRevision of key “Stat 101” messages.
Day 2 – approx. 3 hoursRevision of (univariate) regression analysis: the linear model, generalised linear model.Main packages: lme4.
Day 3 – approx. 3 hoursLinear mixed models, the parametric bootstrap, permutation tests and the bootstrap.Main packages: lme4, mvabund.
Day 4 – approx. 3 hoursModel selection, classical multivariate analysis.Main packages: glmnet.
Day 5 – approx. 3 hoursMultivariate abundance data: hierarchical models, key properties, hypothesis testing.Main packages: mvabund.
Day 6 – approx. 3 hoursMultivariate abundance data: design-based inference for dependent data, indicator species.Main packages: mvabund.
Day 7 – approx. 3 hoursCompositional data, explaining cross-species patterns using traits.Main packages: mvabund.
Day 8 – approx. 3 hoursClassifying species based on environmental response, predictive modelsMain packages: Speciesmix, mvabund, lme4.
Day 9 – approx. 3 hoursModel-based ordination and inferenceMain packages: gllvm.
Day 10 – approx. 3 hoursInferring interactions form co-occurrence dataMain packages: gllvm, ecoCopula.

Course Instructor

Prof. David Warton
Personal website
Work Webpage
ResearchGate
GoogleScholar
David is an ecological statistician who advances methodology for data analysis in ecology to improve the ability of ecologists to answer important research questions with a focus on developing and translating modern statistical approaches to important ecological problems.
His cross-disciplinary research involves evaluating the methods for data analysis currently used in ecology, and where necessary, developing new methodologies to assist ecologists answer key research questions. This has led to contributions to current practice in ecology in multivariate analysis, allometric line-fitting and the analysis of presence-only data.

Introduction To Spatial Analysis Of Ecological Data Using R (ISPEPR)
2025-05-05 - 2025-05-13 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
The aim of the course is to introduce you to a spatial data processing, analysis, and visualization capabilities of the R programming language. It will teach a range of techniques using a mixture of lectures, computer exercises and case studies.
By the end of the course participants should:

Understand the basic concepts of spatial data analysis
Know R’s spatial capabilities
Understand how to import a range of spatial data sources into R
Be confident with using R’s command-line interface (CLI) for spatial data processing
Be able to perform a range of attribute operations (e.g. subsetting and joining), spatial operations (e.g. distance relations, topological relations), and geometry operations (e.g. clipping, aggregations)
Understand coordinate reference systems (CRSs), be able to decide which CRS to use, and how to reproject spatial data
Know how to visualize the results of a spatial analysis in the form of static and interactive maps
Have the confidence to apply spatial analysis skills to their own projects

Intended Audiences
Academics and post-graduate students working on projects related to spatial data and want access to a powerful (geo)statistical and visualization programming language.
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 spatial data analysis and R beginners who have prior experience with geographic data.

Course Details
Last Up-Dated – 26:05:2021
Duration – Approx. 24 hours
ECT’s – Equal to 2 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 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 spatial data analysis and R beginners who have prior experience with geographic data.

Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.
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. 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. 4 HoursIntroduction to the courseKey concepts related to spatial dataR’s spatial ecosystemReading data from spatial file formatsUnderstanding R’s spatial classes

Day 2
Approx. 4 HoursCreating static and interactive maps:Customizing mapsMaking facet mapsCreating animationsUsing specific-purpose mapping packages

Day 3
Approx. 4 HoursAttribute data operations:Vector attribute subsetting, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objects

Day 4
Approx. 4 HoursSpatial data operations:Spatial subsettingTopological relationsSpatial joiningAggregationMap algebraLocal, focal, and zonal raster operations

Day 5
Approx. 4 HoursGeometry operations:Geometric operations on vector dataGeometric operations on raster dataInteractions between rasters and vectors

Day 6
Approx. 4 HoursUnderstanding of the coordinate reference systems (CRSs)Reprojecting geographic dataModifying map projectionsRetrieving open data from web sourcesUsing R packages for spatial data retrievalWriting spatial data

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.
ResearchGate
GoogleScholar
ORCID
LinkedIn
GitHub

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

Bayesian Hierarchical Modelling Using R (IBHMPR)
2025-05-05 - 2025-05-12 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.

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.

Course Details
Last Up-Dated – 11:12:2020
Duration – Approx. 30 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
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 regression methods and generalised linear models.

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. 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 8 hours
Module 1: Simple hierarchical regression modelsModule 2: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models

Day 2
Approx 8 hours
Module 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models

Day 3
Approx 8 hours
Module 5: Hierarchical models vs mixed effects modelsModule 6: Multivariate and multi-layer hierarchical modelsPractical: Advanced examples of hierarchical models

Day 4
Approx 8 hours
Module 7: Shrinkage and variable selectionModule 8: Hierarchical models and partial poolingPractical: Shrinkage modelling

Dr. Andrew Parnell
Works at - Hamilton Institute, Maynooth University
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

Monday 21st
Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files.

Teaches
Stable Isotope MIxing Models Using R (SIMM)
Introduction to Bayesian Hierarchical Modelling (IBHM)
Time Series Data Analysis Using R (TSDA)
Missing Data Analytics Using R (MDAR)

Multivariate Analysis Of Ecological Communities Using R With The VEGAN Package (VGNRPR)
2025-05-05 00:00 - 2025-05-09 00:00 (Europe/London) | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

COURSE FORMAT
Pre Recorded

About This Course
This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”.

Intended Audiences
Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines.

Course Details
Last Up-Dated – 08:10:2021
Duration – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with workshop sessions on R. ~2 modules per day, each module consists of ~1h30/2h lecture + coding, break, ~1h30/2h exercises + summary/discussion. The schedule can be slightly modified according to the interest of the participants. The course will take place online. All the sessions will be video recorded and made available immediately on a private video hosting website as soon as possible after each 2hr session.

Assumed quantitative knowledge
We will assume that you are familiar with basic statistical concepts, linear models, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course).

Assumed computer background
To take full advantage of this course, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands, know how to write code in the RStudio console and script editor, load data from files (txt, xls, csv).

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
• Module 1: Introduction to community data analysis, basics of programming
• Module 2: Diversity analysis, species-abundance distributions
• Module 3: Distance and transformation measures
• Module 4: Clustering and classification analysis
• Module 5: Unconstrained ordinations: Principal Component Analysis
• Module 6: Other unconstrained ordinations
• Module 7: Constrained ordinations: RDA and other canonical analysis
• Module 8: Statistical tests for multivariate data and variation partitioning
• Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods

Antoine Becker-Scarpitta
Works at: CIRAD : CIRAD: The French agricultural research and international cooperation organization working for the sustainable development of tropical and Mediterranean regions.
Teaches:

Multivariate analysis of ecological communities in R with the VEGAN package (VGNR)

Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity, particularly on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.
ResearchGate
Google Scholar
ORCID
GitHub

Stable Isotope Mixing Models Using SIBER, SIAR, MixSIAR (SIMMPR)
2025-05-05 - 2025-05-08 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions.

Intended Audiences
The course is aimed at biologists with a basic to moderate knowledge in R. The course is aimed at anyone (academic or industry) who research is heavily reliant on analysing stable isotope data. There is a strong association with data on food webs and trophic relationships, but the tools learned can be applied to other systems.

Course Details
Last Up-Dated – 28:04:2023
Duration – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

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. 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
Basic concepts.Module 1: Introduction; why use a SIMM?Module 2: An introduction to bayesian statistics.Module 3: Differences between regression models and SIMMs.Practical: Revision on using R to load data, create plots and fit statistical models.Round table discussion: Understanding the output from a Bayesian model.

Day 1
Approx 8 hours
Basic concepts.Module 1: Introduction; why use a SIMM?Module 2: An introduction to bayesian statistics.Module 3: Differences between regression models and SIMMs.Practical: Revision on using R to load data, create plots and fit statistical models.Round table discussion: Understanding the output from a Bayesian model.

Day 2
Approx 8 hours
Understanding and using SIAR.Module 4: Do’s and Don’ts of using SIAR.Module 5: The statistical model behind SIAR.Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.Round table discussion: Issues when using simple SIMMs.

Day 3
Approx 8 hours
SIBER and MixSIAR.Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).Module 7: What are the differences between SIAR and MixSIAR?Practical: Using MixSIAR on real world data sets; benefits over SIAR.Round table discussion: When to use which type of SIMM.

Day 4
Approx 8 hours
Advanced SIMMs.Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.Module 9: Source grouping: when and how?Module 10: Building your own SIMM with JAGS.Practical: Running advanced SIMMs with JAGS.Round table discussion: Bring your own data set.

Dr. Andrew Parnell
Works at: Institute or University: Hamilton Institute, Maynooth University
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

Monday 21st
Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files.

Teaches
Stable Isotope MIxing Models Using R (SIMM)
Introduction to Bayesian Hierarchical Modelling (IBHM)
Time Series Data Analysis Using R (TSDA)
Missing Data Analytics Using R (MDAR)

Introduction To Stan For Bayesian Data Analysis (ISBDPR)
2025-05-05 - 2025-05-07 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
Stan (https://mc-stan.org) is “a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.” Stan is a powerful programming language for developing and fitting custom Bayesian statistical models. In this course, we provide a general introduction to the Stan language, and describe how to use it to develop and run Bayesian models. We begin by first covering the theory behind Stan, which covers Bayesian inference, Markov Chain Monte Carlo (MCMC) for sampling from probability distributions, and the efficient Hamiltonian Monte Carlo (HMC) method that Stan implements. Next, we learn how to write Stan models by creating simple Bayesian such as binomial models and models using normal distributions. In so doing, the basics of the Stan language will be apparent. Although Stan can be used with multiple different type of statistical programs (Python, Julia, Matlab, Stata), we will use Stan with R exclusively, specifically using the rstan or cmdstanr packages. Using thesepackages, we will can compile and sample from a HMC sampler for the Bayesian models we defined, plot and summarize the results, evaluate the models, etc. We then cover some widely used and practically useful models including linear regression, logistic regression, multilevel and mixed effects models. We will end by covering some more complex models, including probabilistic mixture models.

Intended Audiences
This course is aimed at anyone who is in interested in doing advanced Bayesian data analysis using Stan. Stan is a state of the art tool for advanced analysis across all academic scientific disciplines, engineering, and business, and other sectors.

Course Details
Last Up-Dated – 21.01.2022
Duration – 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.
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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and practical experience with linear regression, logistic regression, mixed effects models using R.

Assumed computer background
Some experience and familiarity with R is required. No prior experience with Stan itself is required.

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. 4 Hours
Topic 1: Hamiltonian Monte Carlo for Bayesian inference. We begin by describing Bayesian inference, whose objective is the calculation of a probability distribution over a high dimensional space, namely the posterior distribution. In general, this posterior distribution can not be described analytically, and so to summarize or make predictions from the posterior distribution, we must draw samples from it. For this, we can use Markov Chain Monte Carlo (MCMC) methods including the Metropolis sampler, sometimes known as random-walk Metropolis. Hamiltonian Monte Carlo (HMC), which Stan implements, is ultimately an efficient version of the Metropolis sampler that does not involve random walk behaviour. In this introductory section of the course, we will go through these major theoretical topics in sufficient detail to be able to understand how Stan works.
Topic 2: Univariate models. To learn the Stan language and how to use it to develop Bayesian models, we will start with simple models. In particular, we will look at binomial models and models involving univariate normal distributions. The models will allow us to explore many of the major features of the Stan language, including how to specify priors, in conceptually easy examples. Here, we will also learn how to use rstan and cmdstanr to compile the HMC sampler from the defined Stan model, and draw samples from it.

Day 2
Approx. 4 Hours
Topic 2: Univariate models continued
Topic 3: Regression models. Having learned the basics of Stan using simple models, we now turn to more practically useful examples including linear regression, general linear models with categorical predictor variables, logistic regression, Poisson regression, etc. All of these examples involve the use of similar programming features and specifications, and so they are easily extensible to other regression models.

Day 3
Approx. 4 Hours

Topic 4: Multilevel and mixed effects models. As an extension of the regression models that we consider in the previous topic, here we consider multilevel and mixed effects models. We primarily concentrate on linear mixed effects models, and consider the different ways to specify these models in Stan.
Topic 5: Because Stan is a programming language, it essentially gives us the means to create any bespoke or custom statistical model, and not just those that are widely used. In this final topic, we will cover some more complex cases to illustrate it power. In particular, we will cover probabilistic mixture models, which are a type of latent variable model.

Course Instructor

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Dr. Mark Andrews

Works AtSenior Lecturer, Psychology Department, Nottingham Trent University, England

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

Introduction To Scientific, Numerical, And Data Analysis Programming In Python (PYSCPR)
2025-05-05 - 2025-05-06 | Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of scientific, numerical, and data analysis programming Python. This two day course provides a general introduction to numerical programming in Python, particularly using numpy, data processing in Python using Pandas, data analysis in Python using statsmodels and rpy2. We will also cover the major data visualization and graphics tools in Python, particularly matplotlib, seaborn, and ggplot. Finally, we will cover some other major scientific Python tools, such as for symbolic mathematics and parallel programming and code acceleration. Note that in this course, we will not be teaching Python fundamentals and general purpose programming, but this knowledge will be assumed, and is also provided in a preceding two-day course.

Intended Audiences
This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here, but are covered in a subsequent course, the fundamentals taught here are vital for master data science and scientific applications of Python.

Course Details
Last Up-Dated – 05:05:2022
Duration – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts.
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts, even if all of them are not convenient times. By joining any live sessions that are possible, this will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions. Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better. 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
We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra, calculus,probability distributions. However, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics, or even advanced high school level, can be assumed to havesufficient familiarity with these concepts.

Assumed computer background
We assume familiarity with using Python and knowledge of general purpose programming in Python. This topics are covered comprehensively in a preceding two-day course, which will provide all the prerequisites for this course.

Equipment and software requirements

Attendees of the course should bring a laptop computer with Python (version 3) and the Python packages that we will use (such as numpy, pandas, sympy, etc) installed. All the required software is free and open source and is available on Windows, MacOs, and Linux. Instructions on how to install and configure all the software will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.

Download Python

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. 6 hours
Topic 1: Numerical programming with numpy. Although not part of Python’s official standard library, the numpy package is the part of the de facto standard library for any scientific and numerical programming. Here we will introduce numpy, especially numpy arrays and their built in functions (i.e. “methods”). Here, we will also consider how to speed up numpy code using the Numba just-in-time compiler.
Topic 2: Data processing with pandas. The pandas library provides means to represent and manipulate data frames. Like numpy, pandas can be see as part of the de facto standard library for data oriented uses of Python. Here, we will focus on data wrangling including selecting rows and columns by name and other criteria, applying functions to the selected data, aggregating the data. For this, we will use Pandas directly, and also helper packages like siuba.
Day 2 – approx. 6 hours
Topic 3: Data Visualization. Python provides many options for data visualization. The matplotlib library is a low level plotting library that allows for considerable control of the plot, albeit at the price of a considerable amount ofm low level code. Based on matplotlib, and providing a much higher level interface to the plot, is the seaborn library. This allows us to produce complex data visualizations with a minimal amount of code. Similar to seaborn is ggplot, which is a direct port of the widely used R based visualization library.
Topic 4: Statistical data analysis. In this section, we will describe how to perform widely used statistical analysis in Python. Here we will start with the statsmodels, which provides linear and generalized linear models as well as many other widely used statistical models. We will also cover rpy2, which is and interface from Python to R. This allows us to access all of the the power of R from within Python.
Topic 5: Symbolic mathematics. Symbolic mathematics systems, also known as computer algebra systems, allow us to algebraically manipulate and solve symbolic mathematical expression. In Python, the principal symbolic mathematics library is sympy. This allows us simplify mathematical expressions, compute derivatives, integrals, and limits, solve equations, algebraically manipulate matrices, and more.
Topic 6: Parallel processing. In this section, we will cover how to parallelize code to take advantage of multiple processors. While there are many ways to accomplish this in Python, here we will focus on the multiprocessing

Course Instructor

Dr. Mark Andrews

Works AtSenior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

Making Beautiful And Effective Maps In R (MAPRPR)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

Course
The aim of the course is to show you how to use R to make pretty, yet appealing maps using the R programming language. Several R packages related to spatial data processing and visualization will be introduced during the course. The course will teach you how create publication-ready static maps, animated maps, interactive maps, and simple map applications using a mixture of lectures and computer exercises.
By the end of the course participants should:

Understand the basic concepts behind the tmap package
Be able to create a variety types of static maps, including raster maps, choropleth maps, and point maps
Know how to create interactive maps and simple map applications using the shiny package
Be able to create facet maps and map animations to represent spatiotemporal phenomenon
Know how to utilize specific-purpose mapping packages to create cartograms or grid maps
Have the confidence to apply map making skills to their own projects

Intended Audiences
Academics and post-graduate students working on projects related to spatial data and want to create publication-ready maps, interactive maps for their websites, or simple mapping web applications
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 to quickly create maps for their reports or websites
The course is designed for intermediate R users interested in maps making and R beginners who have prior experience with geographic data.

Course Details
Last Up-Dated – 23:02:23
Duration – Approx. 16 hours
ECT’s – Equal to 2 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.
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. 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. 8 hoursIntroduction to mapping packages in RMaking static mapsApplying point, lines, polygons, and raster map layersCustomizing mapsCreating interactive mapsSaving maps

Day 2
Approx. 8 hoursMaking facet mapsCreating animated mapsMaking inset mapsUsing specific-purpose mapping packagesCreating simple map applicationsOther mapping packages in R

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

Bayesian Approaches To Regression And Mixed Effects Models Using R And brms (BARMPR)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
Bayesian methods are now increasingly widely used for data analysis based on linear and generalized linear models,and multilevel and mixed effects models. The aim of this course is to provide a solid introduction to Bayesian approaches to these topics using R and the brms package. Ultimately, in this course, we aim to show how Bayesian methods provide a very powerful, flexible, and extensible approach to general statistical data analysis. We begin by covering Bayesian approaches to linear regression. We will compare and contrast, in both practical and theoretical terms, the Bayesian approach and classical approach to linear regression. This will allow us to easily identify the major similarities and major differences, both in terms of concepts and practice, between the Bayesian and classical approaches. We will then proceed to Bayesian approaches to generalized linear models, including binary logistic regression, ordinal logistic regression, Poisson regression, zero-inflated models, etc. In this coverage, we will see the very wide range of models to which Bayesian methods can be easily applied. Finally, we will cover Bayesian approaches to multilevel and mixed effects models. Here again, we will see how Bayesian methods allow us to easily extend traditionally used methods like linear and generalized linear mixed effects models. We will also see how Bayesian methods allow us to control model complexity and solve algorithmic problems (e.g. model convergence problems) that can plague classical approaches to multilevel and mixed effects models. Throughout this course, we will be using, via the brms package, Markov Chain Monte Carlo (MCMC) methods. However, full technical details of MCMC will will be described here, but will be provided in subsequent Bayesian data analysis courses.

Intended Audiences
This course is aimed at anyone who is in interested in using Bayesian approaches to regression, multilevel, and mixed effects models in any area of science, including the social sciences, life sciences, physical sciences. No prior experience or familiarity with Bayesian statistics is required.

Course Details
Last Up-Dated – 27:05:2021
Duration – 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.
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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with linear regression, logistic regression, mixed effects models.

Assumed computer background
Some experience and familiarity with R is required. However, although we will be using R extensively, all the code that we use will be made available, and so attendees will usually just need to copy and paste and add minor modifications to this 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. 6 hours
Topic 1: Bayesian linear models. We begin by covering Bayesian linear regression. For this, we will use the brm command from the brms package, and we will compare and contrast the results with the standard lm command. By comparing and contrasting brm with lm we will see all the major similarities and differences between the Bayesian and classical approach to linear regression. We will, for example, see how Bayesian inference and model comparison works in practice and how it differs conceptually and practically from inference and model comparison in classical regression. As part of this coverage of linear models, we will also use categorical predictor variables and explore varying intercept and varying slope linear models.
Topic 2: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that do not always hold in practice. For example, they assume a normal distribution of the residuals, and assume homogeneity of variance of this distribution across all values of the predictors. In Bayesian models, these assumptions are easily relaxed. For example, we will see how we can easily replace the normal distribution ofthe residuals with a t-distribution, which will allow for a regression model that is robust to outliers. Likewise, we can model the variance of the residuals as being dependent on values of predictor variables.
Day 2 – approx. 6 hours
Topic 3: Bayesian generalized linear models. Generalized linear models include models such as logistic regression, including multinomial and ordinal logistic regression, Poisson regression, negative binomialregression, zero-inflated models, and other models. Again, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. In our coverage of this topic, we will see how powerful Bayesian methods are, allowing us to easily extend our models in different ways in order to handle a variety of problems and to use assumptions that are most appropriate for the data being modelled.
Topic 4: Multilevel and mixed models. In this section, we will cover the multilevel and mixed effects variants of the regression models, i.e. linear, logistic, Poisson etc, that we have covered so far. In general, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups, and so on). For this, we use a wide range of real-world data-sets and problems, and move between linear, logistic, etc., models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models, and how to choose between maximal and minimal models. We will also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical problems (e.g. lack of model convergence) that beset classical approaches.

Course Instructor

Dr. Mark Andrews

Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

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Data Wrangling Using R And Rstudio (DWRSPR)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Reocrded

About This Course
In this two day 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 formating and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly, it can be a time consuming, labourious, 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. On Day 1 of this course, having covered how to read data of different types into R, we 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. On Day 2, we 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.

Course Details
Last Up-Dated – 22:04:2021
Duration – 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.
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
We will assume familiarity with only the most basic of statistical concepts, such as descriptive statistics. We will not even assume that participants will have taken university level courses on statistics.

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. 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. 6 Hours
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.
Day 2
Approx. 6 Hours
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.

Course Instructor

Dr. Mark Andrews

Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

Let’s connectLorem ipsum dolor sit amet, consectetuer adipiscing elit.

General Info
info@website.com

Twitter
@website.com

Facebook
website.com

Copyright PR Statistics 2022 | Privacy Policy | Disclaimer | Site Map

Data visualization using GG plot 2 (R and Rstudio) (DVGGPR)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
In this two day course, we provide a comprehensive introduction to data visualization in R using ggplot. On the first day, 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. On Day 2, we begin by covering some additional plot types that are often related but not identical to those major types covered on Day 1: 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.

Course Details
Last Up-Dated – 08:04:2021
Duration – 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.

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
We will assume only a very minimal amount of familiarity with some general statistical concepts. Anyone who has taken any undergraduate (Bachelor’s) level introductory course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.

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. 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. 6 Hours
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.
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.

Day 2
Approx. 6 Hours
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.
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. Mark Andrews

Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

Introduction To Statistics Using R And Rstudio (IRRS03R)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
In this two day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc. We then provide an introduction to data processing and formatting (aka, data wrangling), an introduction to data visualization, an introduction to RMarkdown, and introduce how to some of the most widely used statistical methods such as linear regression, Anovas, etc. From this course, you will gain a comprehensive introduction to R, which will serve as foundation for progressing further with R to any kind of data analysis, data science, or statistics.

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.

Course Details
Last Up-Dated – 18:03:2021
Duration – 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.
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
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.

Assumed computer background
No prior experience with R or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.

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. 6 Hours
Topic 1: The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular.
Topic 2: Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively.
Topic 3: First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier.

Day 2
Approx. 6 Hours
Topic 4: Introducing wrangling. Data wrangling, which is the art of cleaning and restructuring data is a big topic. Here, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Here, we will primarily focus on filtering, slicing, selecting, renaming, and mutating data frames.
Topic 5: Data visualization. Data visualization is another big and important topics. Here, we just provide an introduction, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). We’ll cover scatterplots, boxplots, histograms, and their variants.
Topic 6: RMarkdown. RMarkdown is a powerful tool for creating reproducible research reports, as well as slides, scientific website, posters, etc. In an RMarkdown document, we mix R code and the narrative text of the report, and the outputs of the R code, including figures, are included in the final document.
Topic 7: Introduction to Statistics using R. There are many thousands of statistical methods built into R. Here, we will simply provide an introduction to some of the most widely used methods. In particular, we will cover linear regression, Anova, and some other simple test. The aim of this section is to get a sense of how statistical analysis is done in a R, and how to perform some of the most widely used methods.

Course Instructor

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Dr. Mark Andrews

Works AtSenior Lecturer, Psychology Department, Nottingham Trent University, England

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

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Introduction to Python and Programming in Python (PYINPR)
2025-05-05 - 2025-05-06 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

COURSE FORMAT
Pre Recorded

About this course

Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. In order to use Python confidently and competently for these applications, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment, the Python language, and general purpose programming in Python. We cover how to install and set up a Python computing environment, describing how to set virtual environments, how to use Python package installers, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python, covering all the following major topics: data types and data container types, conditionals, iterations, functional programming, object oriented programming, modules, packages, and imports. Note that in this course, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course, for which the topics covered in this course are a necessary prerequisite.

Intended Audiences

This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here, but are covered in a subsequent course, the fundamentals taught here are vital for master data science and scientific applications of Python.

Course Details
Last Up-Dated – 21:05:2022
Duration – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English

Teaching Format
This course will be hands-on and workshop based. Throughout each day, there will be some brief introductory remarks for each new topic, introducing and explaining key concepts.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better. 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

No particular knowledge of mathematics or statistics is required.

Assumed computer background

No prior experience with Python or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.

Equipment and software requirements

Attendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach.

If you prefer to install and use Python on your machine, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properlY.

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
Approx. 6 Hours
Topic 1: Installing and setting up Python. There are many ways to write and execute code in Python. Which to use depends on personal preference and the type of programming that is being done. Here, we will explore some of the commonly used Integrated Development Environments (IDE) for Python, which include Spyder and PyCharm. Here, we will also introduce Jupyter notebooks, which are widely used for scientific applications of Python, and are an excellent tool for doing reproducible interactive work. Also as part of this topic, we will describe how to use virtual environments and package installers such as pip and conda.
Topic 2: Data Structures. We will begin our coverage of programming with Python by introducing its different data structures.and operations on data structures This will begin with the elementary data types such as integers, floats, Booleans, and strings, and the common operations that can be applied to these data types. We will then proceed to the so-called collection data structures, which primarily include lists, dictionaries, tuples, and sets.
Topic 3: Programming I. Having introduced Python’s data types, we will now turn to how to program in Python. We will begin with iteration, such as the for and while Here, we also cover some of Python’s functional programming features, specifically list, dictionary, and set comprehensions.

Day 2
Approx. 6 Hours
Topic 4: Programming II. Having covered iterations, we now turn to other major programming features in Python, specifically, conditionals, functions, and exceptions.
Topic 5: Object Oriented Programming. Python is an object oriented language and object oriented programming in Python is extensively used in anything beyond the very simplest types of programs. Moreover, compared to other languages, object oriented programming in Python is relatively easy to learn. Here, we provide a comprehensive introduction to object oriented programming in Python.
Topic 6: Modules, packages, and imports. Python is extended by hundreds of thousands of additional packages. Here, we will cover how to install and import these packages, but more importantly, we will show how to write our own modules and packages, which is remarkably easy in Python relative to some programming languages.

Course Instructor

Dr. Mark Andrews

Works AtSenior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

Introduction / Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDAPR)
2025-05-05 | Recorded, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Course Format
Pre Recorded

About This Course
Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses, many researchers do not have opportunities to learn the fundamentals of Bayesian methods, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods, both theoretically and practically. We will teach the fundamental concepts of Bayesian inference and Bayesian modelling, including how Bayesian methods differ from their classical statistics counterparts, and show how to do Bayesian data analysis in practice in R. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian methods: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayesian model selection, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses, specifically general linear models. For these analyses, we will use real world data sets, and carry out the analysis using the brms package in R, which is an excellent and powerful package for Bayesian analysis. In this coverage, we will also provide a brief introduction to Markov Chain Monte Carlo methods, although these will be described in more detail in subsequent Bayesian data analysis courses.

Intended Audiences
This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science, including the social sciences, life sciences, physical sciences. No prior experience or familiarity with Bayesian statistics is required.

Course Details
Last Up-Dated – 20:05:2022
Duration – 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.
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
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with commonly used methods like linear regression, correlation, or t-tests. Most or all of these concepts and methods are covered in a typical undergraduate statistics courses in any of the sciences and related fields.

Assumed computer background
R experience is desirable but not essential. Although we will be using R extensively, all the code that we use will be made available, and so attendees will just need to copy and paste and add minor modifications to this code. Attendees should install R and RStudio and some R packages on their own computers before the workshops, and have some minimal familiarity with the R environment.

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. 6 hours
Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternativeV school of statistics to the traditional approach, which is referred to variously as the classical, or sampling theory based, or frequentist based approach, rather than being a specialized or advanced statistics topic. However, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition, and a pragmatic blend of both approaches is entirely possible.
Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such, it can be used as a means for performing statistical inference. In this section of the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an application of these methods to more complex statistical models, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases.
Topic 3: Bayesian inference in a simple statistical model. In this section, we will work through a classic statistical inference problem, namely inferring the number of red marbles in an urn of red and black marbles, or equivalent problems. This problem is easy to analyse completely with just the use of R, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function, prior distributions, posterior distributions, maximum a posteriori estimation, high posterior density intervals, posterior predictive intervals, marginal likelihoods, Bayes factors, model evaluation of out-of-sample generalization.
Day 2
Approx. 6 hours
Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach, which is referred to variously as the classical, or sampling theory based, or frequentist based approach, rather than being a specialized or advanced statistics topic. However, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition, and a pragmatic blend of both approaches is entirely possible.
Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such, it can be used as a means for performing statistical inference. In this section of the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an application of these methods to more complex statistical models, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases.
Topic 3: Bayesian inference in a simple statistical model. In this section, we will work through a classic statistical inference problem, namely inferring the number of red marbles in an urn of red and black marbles, or equivalent problems. This problem is easy to analyse completely with just the use of R, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function, prior distributions, posterior Distributions, maximum a posteriori estimation, high posterior density intervals, posterior predictive intervals, marginal likelihoods, Bayes factors, model evaluation of out-of-sample generalization.

Course Instructor

Dr. Mark Andrews

Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England

Teaches
Free 1 day intro to r and r studio (FIRR)
Introduction To Statistics Using R And Rstudio (IRRS03)
Introduction to generalised linear models using r and rstudio (IGLM)
Introduction to mixed models using r and rstudio (IMMR)
Nonlinear regression using generalized additive models (GAMR)
Introduction to hidden markov and state space models (HMSS)
Introduction to machine learning and deep learning using r (IMDL)
Model selection and model simplification (MSMS)
Data visualization using gg plot 2 (r and rstudio) (DVGG)
Data wrangling using r and rstudio (DWRS)
Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
Bayesian data analysis (BADA)
Bayesian approaches to regression and mixed effects models using r and brms (BARM)
Introduction to stan for bayesian data analysis (ISBD)
Introduction to unix (UNIX01)
Introduction to python (PYIN03)
Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
Machine learning and deep learning using python (PYML03)
Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

ResearchGate
Google Scholar

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.

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General Info
info@website.com

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ONLINE COURSE - Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live
2025-03-31 - 2025-04-04 | Delivered remotely (Finland), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, March 31st, 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 – Eastern European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.

About This Course
This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”.
This 5-day course covers R concepts, methods, and tools that can be used to analyse community ecology data using (but not limited to) the R package VEGAN. We will cover :

Fundamentals of community ecology,
Diversity indices,
Methods to transform data and calculate distance measures,
Classifications (i.e., clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).
Ordinations (i.e., unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).
Canonical ordinations (i.e., constrained methods) test hypotheses related to multivariate patterns.

In addition the course provides lectures and practices on how to create reproducible workflows and use good programming practices in R.

During the workshops you will follow guided computer coding exercises using either your own data or real empirical datasets to motivate analyses. Exercises include describing patterns along gradients of environmental or anthropogenic disturbance, quantifying the effects of continuous and discrete predictors.

Topics covered during the course include: terrestrial and wetland ecology, microbial ecology, and natural resource management, evolution, palaeoecology.

You are strongly encouraged to use your own datasets (they should be clean and already structured, please contact use if you plan to do this, we will help you to prepare the data). You will benefit from full support in applying multivariate methods to your dataset (defining of the research question, transforming your data, selecting the most appropriate method, carrying out the analysis and interpreting the results).

Intended Audiences
Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines.

Venue
Delivered remotely

Course Details
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with workshop sessions on R.
~2 modules per day, each module consists of ~1h30/2h lecture + coding, break, ~1h30/2h exercises + summary/discussion.
The schedule can be slightly modified according to the interest of the participants.
The course will take place online.
All the sessions will be video recorded and made available immediately on a private video hosting website as soon as possible after each 2hr session.

Assumed quantitative knowledge
We will assume that you are familiar with basic statistical concepts, linear models, and statistical tests (the equivalent of an undergraduate introductory statistics course will be sufficient to follow the course).

Assumed computer background
To take full advantage of this course, minimal prior experience with R is required. Participants should be familiar with basic R syntax and commands, know how to write code in the RStudio console and script editor, load data from files (txt, xls, csv).

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
Classes from 08:00 – 13:00 and 14:00 – 16:00
DAY 1• Module 1: Introduction to community data analysis, basics of programming in R• Module 2: Diversity analysis, species-abundance distributions
Classes from 08:00 – 13:00 and 14:00 – 16:00
DAY 2• Module 3: Distance and transformation measures• Module 4: Clustering and classification analysis
Classes from 08:00 – 13:00 and 14:00 – 16:00
DAY 3• Module 5: Unconstrained ordinations: Principal Component Analysis• Module 6: Other unconstrained ordinations
Classes from 08:00 – 13:00 and 14:00 – 16:00
DAY 4• Module 7: Constrained ordinations: RDA and other canonical analysis• Module 8: Statistical tests for multivariate data and variation partitioning
Classes from 08:00 – 13:00 and 14:00 – 16:00
DAY 5• Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods• Modules 10: Special topics and discussion, analyzing participants’ data.

Antoine Becker-Scarpitta

Antoine is a community ecologist and forest ecologist working as a researcher at The French agricultural research and international cooperation organization, working for the sustainable development of tropical and Mediterranean regions. Antoine was a postdoctoral researcher at the University of Helsinki and the Institute of Botany of the Academy of the Czech Republic. He holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity, particularly on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.
ResearchGate
Google Scholar
ORCID
GitHub

ONLINE COURSE - Stable Isotope Mixing Models using MixSIAR and SIBER (SIMM11) This course will be delivered live
2025-03-25 - 2025-03-28 | Delivered remotely (Ireland), Western European Time, Ireland
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Tuesday, March 25th, 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 – GMT+1 – 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 will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs. We will then focus on the widely used package MixSIAR and SIBER packages. MixSIAR creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). ‘MixSIAR’ is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. ‘MixSIAR’ incorporates several years of advances since ‘MixSIR’ and ‘SIAR’. SIBER fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Participants will be taught the advanced features of these packages, which will enable them to produce a richer class of output. Attendees are encouraged to bring their own data sets and problems to study during the round-table discussions.

Intended Audiences
The course is aimed at biologists with a basic to moderate knowledge in R. The course is aimed at anyone (academic or industry) who research is heavily reliant on analysing stable isotope data. There is a strong association with data on food webs and trophic relationships, but the tools learned can be applied to other systems.

Venue
Delivered remotely

Course Information
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English

Teaching Format
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

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

Tuesday 25th
Classes from 09:30 to 17:30
DAY 1Basic concepts.Module 1: Introduction; why use a SIMM?Module 2: An introduction to bayesian statistics.Module 3: Differences between regression models and SIMMs.Practical: Revision on using R to load data, create plots and fit statistical models.Round table discussion: Understanding the output from a Bayesian model.

Wednesday 26th
Classes from 09:30 to 17:30
DAY 2Understanding and using SIAR.Module 4: Do’s and Don’ts of using SIAR.Module 5: The statistical model behind SIAR.Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.Round table discussion: Issues when using simple SIMMs.

Thursday 27th
Classes from 09:30 to 17:30
DAY 3SIBER and MixSIAR.Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).Module 7: What are the differences between SIAR and MixSIAR?Practical: Using MixSIAR on real world data sets; benefits over SIAR.Round table discussion: When to use which type of SIMM.

Friday 28th
Classes from 09:30 to 17:30
DAY 4Advanced SIMMs.Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.Module 9: Source grouping: when and how?Module 10: Building your own SIMM with JAGS.Practical: Running advanced SIMMs with JAGS.Round table discussion: Bring your own data set.

Andrew Jackson

My research interests lie in understanding ecological systems from an evolutionary perspective. I tend to approach these questions by using computational / mathematical models to understand how the nuts and bolts of these systems work. Much of my current research focuses on understanding predator-prey interactions and how large fish use their spatial environment. My interests also extend to community ecology where the challenge is to understand how communities of organisms and species compete and interact with what is often a self-organising and stable system.
Research GateGoogle ScholarORCIDHomepageGitHub

ONLINE COURSE - Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live
2025-03-11 - 2025-03-13 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Tuesday, March 11th, 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.

About This Course
This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects 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.

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
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 (GMT+0) 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 live sessions attendees will be able 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
We will assume familiarity with general statistical concepts, linear models, statistical inference (p-values, confidence intervals, etc). Anyone who has taken undergraduate (Bachelor’s) level introductory courses on (applied) statistics can be assumed to have sufficient familiarity with these concepts.

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. 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

Tuesday 11th
Classes from 16:00 to 19:00
Topic 1: 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 2: 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.

Wednesday 12th
Classes from 16:00 to 19:00
Topic 3: 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 4: 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 5: 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.

Thursday 13th
Classes from 16:00 to 19:00
Topic 6: 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 7: 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 8: 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.

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 - Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live
2025-02-26 - 2025-02-28 | Delivered remotely (Finland), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Wednesday, February 26th, 2022

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 – Eastern 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 community analytics course is designed for students who have recently started their projects or researchers who are starting using the R ecosystem. During this three-day course, we will cover the basic concepts of multivariate analysis and their implementation in R. This course is a complement to the PR Statistic offering allowing also beginners and non-programmers to discover the statistical tools needed to analyze an ecological dataset in research, natural resource management or conservation context. This course is not geared toward any particular taxonomic group or ecological system.
We will cover diversity indices, distance measures and multivariate distance-based methods, clustering, classification, and ordination techniques. We will focus on the concept of the methods and their implementation on R using different R packages. We will use real-world examples to implement analyses, such as describing patterns along gradients of environmental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors, data mining. The course will consist of lectures, work on R code scripts, and exercises for participants.
PR stats also deliver a more advanced course on analysing community data
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)

Intended Audiences
Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in learning multivariate statistics.

Venue
Delivered remotely

Course Details
Time Zone – Eastern Standard Time
Availability – 20 places
Duration – 3 days
Contact hours – Approx. 21 hours
ECT’s – Equal to 2 ECT’s
Language – English

Teaching Format
The course will be divided into theoretical lectures to introduce and explain key concepts and theories, and practices with workshop sessions on R.
~2 modules per day, each module consists of ~1h30/2h lecture + coding, break, ~1h30/2h exercises + summary/discussion.
The schedule can be slightly modified according to the interest of the participants.
The course will take place online. All the sessions will be video recorded and made available immediately on a private video hosting website as soon as possible after each 2hr session.

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.

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
This 3-day course will explores the nature of community data, and how they are transformed and analysed them in the context of ecological research projects. The course will explore the numerical tools used to describe ecological communities, with a particular focus on R.
Wednesday 26th – Classes from 09:00-17:00
– Classifications (i.e., clustering methods) organise the data into synthetic groups and present them in a tree (dendrogram).
Thursday 27th – Classes from 09:00-17:00
– Ordinations (i.e., unconstrained methods) reveal the multivariate dimension in only a few dimensions (axes).
Friday 28th – Classes from 09:00-17:00
– Canonical ordinations (i.e., constrained methods) test hypotheses related to multivariate patterns.

Course Instructor

Dr. Antoine Becker-Scarpitta
Works at – University of HelsinkTeaches – Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
Antoine is a plant community ecologist working as a postdoctoral researcher at the University of Helsinki and as a postdoctoral fellow at the Institute of Botany of the Academy of the Czech Republic. Antoine holds a degree in Conservation Biology from the University of Paris-Sud-Orsay, and from the Natural History Museum of Paris, he obtained his PhD in Biology/Ecology from the University of Sherbrooke (Canada). Antoine’s research focuses on the temporal dynamics of biodiversity with a particular focus on the forest and Arctic vegetation. Antoine has taught community ecology, plant ecology and evolution, linear and multivariate statistics assisted on R.

ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live
2025-02-25 - 2025-02-27 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Tuesday, February 25th, 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
COURSE DETAILS
This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. 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.

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

Tuesday 25th
Classes from 16:00 to 19:00
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 regresion 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.

Wednesday 26th
Classes from 16:00 to 19:00
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. Like ordinal logistic regression, categorical logistic regression is also 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 27th
Classes from 16:00 to 19:00
Topic 6: 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 7: 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.

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 – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live
2025-02-17 - 2025-02-21 | Delivered remotely (United Kingdom), Western European Time, United Kingdom
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Monday, February 17th, 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
Course overview:
Ecological remote sensing 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 by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles, 2) remotely sensed data gathering and analysis, 3) 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 ecosystems with remote sensing data• be able to report in LaTeX and R Markdown the achieved results

Intended Audiences
Intended Audience• Practitioners, students, academics• People new to R

Venue
Delivered remotely

Course Information
Time zone – Central European Time
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 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

Package needed for the course:– 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 17th – 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]
[Reference systems: introduction to the main coordinate systems]
– Visualizing data
[Visualizing multi- e hyper-spectral data]

Tuesday 18th – Classes from 09:30 to 17:30
– Spectral indices extracted from satellite imagery
[Main spectral indices extracted from remote sensing data]
– Remote sensing data classification
[Generating land cover maps from remotely sensed data]

Wednesday 19th – Classes from 09:30 to 17:30
– Land use change in space and time
[Analysis ecosystem change in space and time: the case of Mato Grosso]
[Time series: ice melt in Greenland]

Thursday 20th – 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
[Ecosystem variability]
[Multivariate analysis on remotely sensed data]

Friday 21st – 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…

ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live
2025-02-11 - 2025-02-13 | Delivered remotely (Portugal), Portugal
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live

Event Date
Tuesday, February 11th, 2025

Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees
through the accompanying computer practicals via video link, so a good internet connection is
essential.
Time Zone
Lisbon (Portugal) time, i.e. UTC / GMT or BST, depending on time of year (daylight saving time
from last Sunday of March to last Sunday of October). Check online for the time conversion
corresponding to the course dates. 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
This course focuses on the use of BART (Bayesian Additive Regression Trees) for modellingspecies’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM), as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally, BART allows mapping the uncertainty and credible intervals associated to each local prediction.
The course includes a combination of theoretical lectures and hands-on practicals in R, as well asopen discussions about models and data for SDM applications. The practicals go through acomplete worked example, from data preparation to model output analysis, with annotated Rscripts that can be adapted on-the-spot by participants to work on their own species of interest.Along the course, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations.

Intended Audiences
The course is aimed at students, researchers and practitioners with an interest in implementing
best practices and state-of-the-art methods for modelling species’ distributions or ecological
niches, in an automated and reproducible way.

Venue
Delivered remotely

Course Details
Availability – 18 places
Duration – 3 days
Contact hours – Approx. 12 hours live, plus remote assistance via Slack from the first day to the
weekday after the course.
ECT’s – Equal to 1.5 ECT’s
Language – English

Teaching Format
This course runs along 3 days, each with a 4-hour live online session. Each session is divided into4 parts, alternating between theoretical lectures and hands-on practicals. Annotated scripts areprovided and instructor assistance is available, both during the live sessions (on Zoom) andwhenever possible the rest of the day (on Slack), until the weekday after the course.Live sessions will be video-recorded, uploaded to a video hosting website as soon as possible aftereach session, and remain available for one month after the course.

Assumed quantitative knowledge
Participants should know what species distribution or ecological niche models (SDM / ENM) are,
and ideally have some previous experience with the basics. Previous knowledge of Bayesian
statistics is not required.

Assumed computer background
Participants should have some previous experience with R, including package installation and
basic data handling, although commented scripts will be provided for the entire course.

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

Tuesday 11th
Classes from 14:00 – 18:00
DAY 1– Module 1a: Obtain and process data, including species presences and environmental variables– Practical– Module 1b: Determine an adequate spatial resolution and extent for modelling– Practical

Wednesday 12th
Classes from 14:00 – 18:00 CET
DAY 2– Module 2a: Build a species distribution model with BART and obtain predictions of environmentalfavorability, with credibility intervals and associated uncertainty– Practical– Module 2b: Evaluate and cross-validate the model, assessing various aspects of predictive ability– Practical

Thursday 13th
Classes from 14:00 – 18:00 CET
DAY 3
– Module 3a: Quantify variable contributions and try out different methods for selecting relevantvariables– Practical– Module 3b: Plot and map the species’ partial response to each variable– Practical

Dr. Marcia Barbosa

Márcia is an experienced researcher and instructor in biogeography and macroecology, particularly in geographic information systems and species distribution modelling. She’s also a reviewer and editor for scientific journals and funding agencies, and a promoter and developer of free and open-source software implementing transparency, reproducibility and best practices. You can see her publication list at her website or at Publons/ResearcherID, Scopus, ORCID, Google Scholar, or ResearchGate.
Research Gate
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Homepage

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 – Phylogenetic Species Distribution Modelling using R (PSDM01) 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 – Phylogenetic Species Distribution Modelling using R (PSDM01) 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 – Phylogenetic Species Distribution Modelling using R (PSDM01) 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
