BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20251026T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20261025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20270328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20271031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20280326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20281029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20290325T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20291028T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20300331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20301027T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20251006 DTEND;VALUE=DATE:20251011 DTSTAMP:20260508T000238 CREATED:20240613T132140Z LAST-MODIFIED:20241114T124438Z UID:10000460-1759708800-1760140799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 6th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \nTime Zone\nTIME ZONE – GMT – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About this course\n 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. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts of bioacoustics and how animal acoustic signals are analyzed\nGain proficiency in handling and manipulating acoustic data in R\, including working with ‘wave’ objects and other audio formats\nDevelop skills in building and interpreting spectrograms using Fourier transform techniques and the seewave package in R\nImport Raven Pro annotations into R and refine these annotations with warbleR functions\nUnderstand how to quantify the structure of acoustic signals through various approaches\nGain experience in quality control of recordings and annotations\, ensuring data integrity and accuracy\nCompare different methods for quantifying acoustic signal structure and understand the implications of each approach\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students conducting research in bioacoustics\, animal behavior\, ecology\, or related fields\nApplied researchers and analysts in public\, private\, or non-profit organizations who require robust\, reproducible\, and flexible tools for analyzing acoustic data\nCurrent R users seeking to expand their knowledge into the field of bioacoustics and learn how to utilize specialized packages for acoustic analysis\nWildlife biologists\, and conservationists interested in leveraging bioacoustic methods for species monitoring and behavioral studies\nData scientists and programmers interested in applying their coding skills to the analysis of animal acoustic signals\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone – GMT \nAvailability – 20 places \nDuration – 5 days\, 4 hours per day \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n \n \n \n \n \n Assumed computer background\n Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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.\n \n \n \n \n \n \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Programme\n \n \n \n \n \n \n \n \n \n \n Monday 6th\n Day 1 – Classes from 13:30 – 17:30Introduction \n– 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. \n– 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. \n– 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. \nWhat 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. \n– 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. \n– Acoustic data in RIntroduction to handling and analyzing acoustic data in R. This includes importing sound files\,basic data exploration\, and visualization techniques. \n– ‘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. \n– ‘wave’ object manipulationsTechniques for manipulating ‘wave’ objects\, including trimming\, concatenating\, and modifyingsound files. Practical exercises will be provided to reinforce these concepts. \n– Additional formatsOverview of other audio file formats (e.g.\, MP3\, FLAC) and how they can be converted and used inR for bioacoustic analysis. \n \n \n \n \n \n Tuesday 7th\n Day 2 – Classes from 13:30 – 17:30 \nBuilding 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. \n– 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. \n– Characteristics and limitationsDiscussion on the strengths and limitations of spectrograms\, including resolution trade-offs andpotential artifacts. Participants will learn to critically evaluate spectrograms. \n– 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 \n– 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. \n– 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. \n– 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. \n– Acoustic measurementsUsing the seewave package to perform detailed acoustic measurements\, such as peak frequency\,dominant frequency\, and frequency range. Practical examples will be provided. \n \n \n \n \n \n Wednesday 8th\n Day 3 – Classes from 13:30 – 17:30 \nAnnotations– 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. \n– Introduction to selections and measurementsInstruction on how to make selections within sound files and take basic measurements such asduration and frequency using Raven Pro. \n– 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. \n– Using annotationsTechniques for annotating sound files in Raven Pro\, including the use of labels and notes to marksignificant events or features within the recordings. \nQuantifying 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. \n– Parameter descriptionDetailed explanation of key acoustic parameters\, such as duration\, frequency range\, andamplitude\, and how they are used to describe sound signals. \n– Harmonic contentTechniques for analyzing the harmonic content of signals\, including identifying harmonic seriesand measuring harmonic-to-noise ratios. \n– 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. \n– Cross-correlation (cross_correlation())Explanation of cross-correlation techniques for comparing sound signals. Participants will usecross_correlation() to measure the similarity between different recordings. \n– 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. \n– 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. \n– Inflections (inflections())Identifying and measuring inflections in sound signals\, which can indicate changes in pitch orother dynamic features. \n– 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. \n \n \n \n \n \n Thursday 9th\n Day 4 – Classes from 13:30 – 17:30 \nQuality control in recordings and annotations– Create catalogsCompiling catalogs of annotated sound files\, which can be used for further analysis or asreference materials. \n– 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. \n– 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. \n– Double-checking selection tables (check_sels()\, spectrograms()\, full_spectrograms() &catalog())Methods for verifying and refining selection tables\, ensuring that all annotations are accurate andcomprehensive. \n– 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 \n– Creating ‘song’ spectrograms (full_spectrograms()\, spectrograms())Building spectrograms that represent entire songs or sequences of vocalizations\, providing ahigher-level view of acoustic patterns. \n– ‘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. \n \n \n \n \n \n Friday 10th\n Day 5 – Classes from 13:30 – 17:30 \nChoosing 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 \n– Intro to PhenotypeSpaceIntroduction to the concept of acoustic spaces and the PhenotypeSpace framework\, which allowsfor the visualization and comparison of acoustic diversity. \n– Quantifying space sizeTechniques for measuring the size of acoustic spaces\, which can provide insights into thevariability and complexity of vocalizations. \n– Comparing sub-spacesMethods for comparing different sub-spaces within the overall acoustic space\, allowing for theanalysis of variations between species\, populations\, or other groups. \nEach 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. \n \n \n \n \n \n \n Course Instructor\n \n*\nDr. Marcelo Araya Salas\nWorks at – Neuroscience Research Center\, Universidad de Costa Rica \nMarcelo 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. \nResearchGate \nGoogle Scholar \nWork Homepage \nPersonal Homepage URL:https://prstats.preprodw.com/course/bioacoustics-data-analysis-biac05/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BIAC02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20251020 DTEND;VALUE=DATE:20251031 DTSTAMP:20260508T000239 CREATED:20231213T115657Z LAST-MODIFIED:20250205T150304Z UID:10000364-1760918400-1761868799@prstats.preprodw.com SUMMARY:ONLINE COURSE - Path analysis\, structural equations and causal inference for biologists (PSCB03) This course will be delivered live DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, October 20th\, 2025\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nCOURSE PROGRAM\nTIME ZONE – Eastern Daylight Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \nBy 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 \nParticipants 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. \n \n \n \n \n \n Intended Audiences\n Scientists generally\, and ecologists specifically\, who want to test hypotheses concerning cause-and-effect relationships involving several variables\, especially involving observational data. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – TBC \nDuration – 7 x 1/2 days \nContact hours – Approx. 25 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n Assumed quantative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n Proficiency with R programming language\, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g.\, lm()).\n \n \n \n \n \n Equipment and software requirements\n 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. \nhttps://cran.r-project.org/Download RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations 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 \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Monday 20th\n 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 \n \n \n \n \n \n Tuesday 21st\n 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 \nPractical work (2h) \n \n \n \n \n \n Wednesday 22nd\n Day 3 8:30 – 12:00Path analysis using piecewiseSEM (2h30) The piecewiseSEM library in R \nPractical work (1h) \n  \n \n \n \n \n \n Thursday 23rd\n 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 \nPractical work (1h30) \n \n \n \n \n \n Friday 24th\n 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 \nCovariance-based path analysis using lavaan (1h30) \n \n \n \n \n \n Monday 27th\n Day 6 08:30 – 11:30 \nLatent 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 \n \n \n \n \n \n Tuesday 28th\n Day 7 08:30 – 12:00 \nPractical using measurement models (1h) \nThe 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 \n \n \n \n \n \n Wednesday 29th\n Day 8 08:30 – 12:00Multigroup models (2h) What is causal heterogeneity? The concept of nested models How to fit multigroup models in lavaan \nPractical: putting everything together (1h30) \n \n \n \n \n \n Thursday 30th\n Day 9 9:00 – 12:00Practical and group presentations of results \n \n \n \n \n \n \n \n \n Bill Shipley\n \n Bill Shipley is an experienced researcher and  \nteacher in plant ecology and statistical ecology.  He has published four scientific monographs and over 170 peer-reviewed papers. URL:https://prstats.preprodw.com/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb03/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:All Live Courses,Home Courses,Live Online Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/03/donna-ruiz-Pe_SZd-oA_0-unsplash-scaled.jpg GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260508T000239 CREATED:20220310T142119Z LAST-MODIFIED:20230727T111252Z UID:10000374-1893456000-1893542399@prstats.preprodw.com SUMMARY:Adapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJPR) DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE FORMAT\nPre-Recorded\n \n \n \n \n \n \n \n \n \n \n About This Course\n R statistical software is becoming increasingly popular for spatial analysis and mapping. This is partially due to a large number of R packages devoted to applying various spatial methods. These packages\, however\, are being revised\, updated\, or even superseded to allow for better performance\, simpler user interface\, or expanded capabilities. Substantial recent changes in R spatial packages include developing the ‘sf’ package as a successor of ‘sp’\, creation of `terra` as a successor of `raster`\, and establishing the `stars` package. Additionally\, all of these packages were affected by the recent major updates of the PROJ library. In this course\, we will learn to use key packages for the analysis of spatial data\, both vector (‘sf’) and raster (‘terra’)\, and see how they differ from their older counterparts\, ‘sp’ and ‘raster’. Another important aspect of the course will be to understood spatial projections and coordinate systems\, how the recent PROJ changes affect R users\, and how to adjust to them. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind spatial analysis ecosystem in R\nKnow how packages such as sp/rgeos/rgdal/raster differ from their successors sf/terra/star\nBe able to switch from using packages such as sp/rgeos/rgdal/raster to sf/terra/stars\nUnderstood the basic concepts behind spatial projections\, and how PROJ.7 differs from PROJ4\nKnow how to deal with coordinate reference systems in R\nHave the confidence to switch from PROJ4 to PROJ7 (i.e.\, for instance\, adjusting old scripts based on PROJ4)?\n\n \n \n \n \n \n Intended Audiences\n \n\n\nAcademics and post-graduate students working on projects related to spatial data\nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R for spatial data analysis\nCurrent R users wanting to update your knowledge\, including switch from using `sp` to `sf`\, and from `raster` to `terra`\n\n\n\nThe course is designed for intermediate R users interested in understanding modern tools for spatial data analysis in R and R beginners who have prior experience with geographic data and other spatial software.\n \n \n \n \n \n Course Details\n Last Up-Dated – 08:12:2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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.\n \n \n \n \n \n Assumed quantitative knowledge\n Understanding basic GIS concepts\, such as spatial vector\, spatial raster\, coordinate reference systems would be beneficial\, but is not necessary.\n \n \n \n \n \n Assumed computer background\n Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. The experience of using some basic R spatial packages\, such as sp or raster would be beneficial. \nHowever\, 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.\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations 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. \n​\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 7.5 hours \nOverview of spatial analysis ecosystem in R\n• available R packages for spatial analysis in R\n• how do R packages represent spatial objects\, and how are they connected with each other\n• importance of using the more recent R spatial packages\, such as ‘sf’ or ‘terra’\n• main concepts behind map projections (geoids\, datums\, geographic/projected coordinates\, types of projections\, etc.)\n• implementation of these concepts in the PROJ library (used by most R spatial packages)\n• differences between PROJ.4 and its newer versions (e.g. PROJ.7)\nSpatial vector data analysis in R\n• spatial vector data processing & analysis in R\n• read/write/and visualize spatial vector data\n• differences between ‘sp’/’rgdal’/’rgeos’ and ‘sf’\n• moving from ‘sp’ to ‘sf’ for spatial vector data processing & analysis\n• spherical geometry: how this concept was recently implemented in sf\, and what is an impact of this implementation\n \n \n \n \n \n Day 2\n Approx. 7.5 hours \nSpatial raster data analysis in R\n• spatial raster data processing & analysis in R\n• read/write/and visualize spatial raster data\n• differences between ‘raster’ and ‘stars’/’terra’\n• moving from ‘raster’ to ‘terra’ for spatial raster data processing & analysis\n• short overview of package ‘stars’\nCoordinate reference systems\n• how to switch from PROJ.4 to PROJ.7 in R\n• open session: questions from the participants\n \n \n \n \n \n \n \n \n Jakub Nowosad\n Works at: Adam Mickiewicz University \n 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. \nResearchGate\nGoogleScholar\nORCID\nLinkedIn\nGitHub\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ\n\n \n \n \n \n \n Teaches\n \nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ URL:https://prstats.preprodw.com/course/adapting-to-the-recent-changes-in-r-spatial-packages-sf-terra-proj-library-projpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PROJ02R.png GEO:53.1423672;-7.6920536 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260508T000239 CREATED:20230322T193501Z LAST-MODIFIED:20240404T141707Z UID:10000424-1893456000-1893542399@prstats.preprodw.com SUMMARY:Reproducible and collaborative data analysis with R (RACRPR) DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n \n\n\nThe computational part of a research is considered reproducible when other scientists (including ourselves in the future) can obtain identical results using the same code\, data\, workflow and software. Research results are often based on complex statistical analyses which make use of various software. In this context\, it becomes rather difficult to guarantee the reproducibility of the research\, which is increasingly considered a requirement to assess the validity of scientific claims. Moreover\, reproducibility is not only important for findings published in academic journals. It also becomes relevant for sharing analyses within a team\, with external collaborators and with one’s supervisor. During this three-day course\, the participants will be introduced to a suite of tools they can use in combination with R to make reproducible the computational part of their own research. A strong emphasis is given to collaboration\, and participants will learn how to set up a project to work with other people in an efficient way. \nOn day 1 the participants learn about the most important aspects that make research reproducible\, which go beyond simply sharing R code. This includes problems arising from the use of different packages versions\, R versions\, and operating systems. The concept of research compendium is introduced and proposed as general framework to organise any research project. Day 2 is dedicated to version control with Git and GitHub which are fundamental tools for keeping track of code changes and for collaborating with other people on the same project. We will cover both\, basic and more advanced features\, like tagging\, branching\, and merging. On day 3 the participants are introduced to literate programming using RMarkdown with the focus on writing a scientific article. The aim is to bind the outputs of the R analysis (i.e. results\, tables\, and figures) together with the text of the article. Participants will also learn how to use templates to fulfil requirements of different journals. \n\n\n\n \n \n \n \n \n Intended Audiences\n This course is suitable for any MSc and PhD students\, postdocs and practitioners from any research field interested in collaborative projects and delivering reproducible results using R.\n \n \n \n \n \n Course Details\n Last up-dated – 13:06:2023 \nDuration –  Approx. 18 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n  \n \n \n \n \n \n Teaching Format\n \n\n\n\n\n\nOn each day\, participants will get an introduction to a different tool and practice its use together with the instructor. There will be lecture-style presentations to explain the different problems that make research not reproducible and provide possible solutions to the problem. Lectures will be alternated with hands-on sections guided by the instructor and group exercises to enhance collaboration skills. \n\n\n\n\n\n\n \n \n \n \n \n Assumed quantitative knowledge\n \n\n\nA basic knowledge of statistics is required. \n\n\n\n \n \n \n \n \n Assumed computer background\n 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.\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). Participants should also create a GitHub account in order to attend the second day of this course. Instructions on how to create the account and how to install Git will be provided during the first day. \n\n\n\n\n\n\nA 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. \n\n\n\n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 6 hours \n\n\n\n\n–  Intro to the reproducibility crisis\n–  Examples of problems arising from different Operating Systems\, R versions\, andpackage versions\n–  What happens when you start R\n–  RStudio projects\n–  Project organization\n–  Code style\n–  Reproducible R environment\n\n\n\n\n\n\n \n\n\n \n \n \n \n \n Day 2\n Approx. 6 hours \n\n\n\n\n–  Intro to Git and Github\n–  Configure Git and GitHub\n\n\n\n\n\n\n\n\n–  Git basic from command line\n–  Create a local repository and push it on Github\n–  Craft a good commit\n–  Clone and fork a GitHub repository\n–  Craft a pull request\n–  Git branch\, merge\, and tag\n–  Git checkout\, reset\, and revert\n–  Use Git with RStudio\n–  Ignore files\n\n\n\n\n \n \n \n \n \n Day 3\n Approx. 6 hours \n\n–  Literate programming\n–  RMarkdown to produce html\, word\, and pdf outputs\n–  Manage references with Zotero\n–  Use templates for word outputs\n–  Write your scientific article with RMarkdown\n–  Reference tables and figures in the text\n\n \n \n \n \n \n \n \n \n Dr. Sergio Vignali\n \n Sergio Vignali is a postdoctoral researcher at the University of Bern (Switzerland)\, in the division of Conservation Biology of the Institute of Ecology and Evolution. His research focuses on spatial predictive models for animal movements and distributions. Sergio combines his strong scientific interest in animal ecology\, particularly birds\, with his computational and statistical background to develop new methodological approaches. He is the developer of SDMtune\, an R package to tune and evaluate species distribution models. Sergio is also an advocate of open source software and is committed to improving transparency and reproducibility in research. \nResearchGate GoogleScholar ORCID GitHub URL:https://prstats.preprodw.com/course/reproducible-and-collaborative-data-analysis-with-r-racrpr/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/07/andrea-lightfoot-Pj6fYNRzRT0-unsplash-scaled.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260508T000239 CREATED:20230322T195810Z LAST-MODIFIED:20230727T114344Z UID:10000426-1893456000-1893542399@prstats.preprodw.com SUMMARY:Advanced Ecological Niche Modelling Using R (ANMRPR) DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis 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. \nTime Zone\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n \n \n \n \n \n \n \n \n \n \n About This Course\n Ecological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nHave you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. \nBy the end of 5-day practical course you will have the capacity to \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others.\n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n \n \n \n \n \n Course Details\n Last up-dated – 29:01:2021 \nDuration – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphic(http://www.r-project.org/).\n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of ecological niche models and biogeography in general is required.\n \n \n \n \n \n Assumed computer background\n Experience implementing ecological niche models using R is desirable.\n \n \n \n \n \n Equipment and software requirements\n \nA 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/. \n\n\nAll 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. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Day 1\n Approx. 7 hours \n\nENM guide: how to model.\nENM R packages.\nSources of environmental variables using dismo package.\nGetting species records with rgbif package.\n\n \n \n \n \n \n Day 2\n Approx. 7 hours \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/sp Thin packages.\nChoosing pseudo-absences with Biomod2 package.\n\n \n \n \n \n \n Day 3\n Approx. 7 hours \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.\nComparing correlative models with AIC\, with ENMeval package.\nValidate models null models.\n\n \n \n \n \n \n Day 4\n Approx. 7 hours \n\nMESS practice with Biomod2 package.\nVirtualSpecies SDMvspecies packages.\nMIGCLIM practice.\n\n \n \n \n \n \n Day 5\n \nMechanistic model NicheMapper packages.\n\n \n \n \n \n \n \n \n \n Dr. Neftali Sillero\n \n Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website\nWork Webpage\nResearchGate\nGoogleScholar\n \n \n \n \n \n \n \n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n \n \n \n \n \n Teaches\n \nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM) URL:https://prstats.preprodw.com/course/advanced-ecological-niche-modelling-using-r-anmrpr/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/07/ANMR011.jpg GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20300101 DTEND;VALUE=DATE:20300102 DTSTAMP:20260508T000239 CREATED:20230322T204322Z LAST-MODIFIED:20230727T121901Z UID:10000427-1893456000-1893542399@prstats.preprodw.com SUMMARY:Structural Equation Modelling for Ecologists and Evolutionary Biologists (SEMRPR) DESCRIPTION:ONLINE COURSE – Hidden Markov Models for movement\, acceleration and other ecological data – an introduction using moveHMM and momentuHMM in R (HMMM01) This course will be delivered live\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n The course is a primer on structural equation modelling (SEM) and confirmatory path analysis\, with an emphasis on practical skills and applications to real-world data. \nStructural equation modelling is a rapidly growing technique in ecology and evolution that unites multiple hypotheses in a single causal network. It provides an intuitive graphical representation of relationships among variables\, underpinned by well-described mathematical estimation procedures. Several advances in SEM over the past few years have expanded its utility for typical ecological datasets\, which include count data\, missing observations\, nested or hierarchical designs\, and true non-linear implementations. \nWe will cover the basic philosophy behind SEM\, provide approachable mathematical explanations of the techniques\, and cover recent extensions that better unite the multiple methods of SEM. Along the way\, we will work through many examples from the primary literature using the open-source statistical software R (www.r-project.org). We will draw on two popular R packages for conducting SEM\, including lavaan and piecewiseSEM. \nParticipants 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.\n \n \n \n \n \n Intended Audiences\n This course is orientated to PhD and MSc students\, as well as persons in research or industry working on ecological data. \n \n \n \n \n \n Course Details\n Last up-dated – 10:03:2023 \nDuration – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n Introductory lectures on the concepts and mathematics of SEM; practical lectures demonstrating the application to real datasets; computer labs to expand on practical lecture materials. Participants are encouraged to bring their own data and develop their own models. Time will be set aside at the end of each day to work with participants on their models. Datasets will be made available for those who do not have existing data to bring.\n \n \n \n \n \n Assumed quantative knowledge\n Basic knowledge of linear modelling.\n \n \n \n \n \n Assumed computer background\n Proficiency with R programming language\, including: importing/exporting data; manipulating data in the R environment; constructing and evaluating basic statistical models (e.g.\, lm()).\n \n \n \n \n \n Equipment and software requirements\n Attendees of the course must use a computer with R/RStudio installed\, as well as the necessary additional R packages. Instructions on how to install the software will be provided before the start of the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/\nDownload RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Tickets \n \n \n \n \n \n \n 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. \n\n\n\n \n Tickets are no longer available\n\n\n \n \n \n \n \n\n\n\n\n\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 7 hours \nIntroduction to SEMModule 1: What is Structural Equation Modeling? Why would I use it?Module 2: Creating multivariate causal modelsModule 3: Fitting piecewise modelsReadings: Grace 2010 (overview)\, Whalen et al. 2013 (example) \n  \n \n \n \n \n \n Day 2\n Approx. 7 hours \nSEM Using LikelihoodModule 4: Fitting Observed Variable models with covariance structures
Module 5: What does it mean to evaluate a multivariate hypothesis?Module 6: Latent Variable models 
Module 7: ANCOVA revisited & NonlinearitiesReadings: Grace & Bollen 2005\, Shipley 2004Optional Reading: Pearl 2012\, Pearl 2009 (causality) \n  \n \n \n \n \n \n Day 3\n Approx. 7 hours \nPiecewise SEMModule 8: Introduction to piecewise approachModule 9: Incorporation of random effects modelsModel 10: Autocorrelation 
Reading: Shipley 2009; Lefcheck 2016 \n  \n \n \n \n \n \n Day 4\n Approx. 7 hours \nAdvanced Topics with Likelihood and Piecewise SEMModule 11: Multigroup models and non-linearitiesModule 12: Composite VariablesModule 13: Phylogenetically-correlated dataModule 14: Prediction using SEMModule 15: How To Reject A Paper That Uses SEMReadings: Grace & Julia 1999\, von Hardenberg & Gonzalez‐Voyer 2013 \n  \n \n \n \n \n \n Day 5\n Approx. 3.5 hours \nOpen Lab and Final Presentations \n  \n \n \n \n \n \n \n Course Instructor\n \nDr. Jon Lefcheck\nDr. Jarett Byrnes URL:https://prstats.preprodw.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semrpr/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SEMR03R.png GEO:53.1423672;-7.6920536 END:VEVENT END:VCALENDAR