BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics 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 END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250510 DTSTAMP:20260419T050722 CREATED:20220221T225153Z LAST-MODIFIED:20240130T173121Z UID:10000359-1746432000-1746810000@prstats.preprodw.com SUMMARY:Movement Ecology (MOVEPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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\n Pre Recorded \n \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 17:03:2023 \nDuration – Approx. 40 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 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 Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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\nDay 1 – approx. 8 hours \nMovement 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. \nDay 2 – approx. 8 hours \nHome 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. \nDay 3 – approx. 8 hours \nDynamic 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 \nDay 4 – approx. 8 hours \nIntroduction 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 \nDay 5 – approx. 8 hours \nStep-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. \n\n  \n \n \n \n \n \n Course Instructor\n \nProf. Luca Borger \nTeaches: \n\nMovement Ecology (MOVE)\n\nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \nLuc Borger… \nCourse Instructor\n \nDr. Jarrett Street \nTeaches: \n\nMovement Ecology (MOVE\n\nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \nGarrett Street… \n  URL:https://prstats.preprodw.com/course/movement-ecology-movepr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MOVE04R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250517 DTSTAMP:20260419T050723 CREATED:20220222T014111Z LAST-MODIFIED:20230727T151646Z UID:10000365-1746403200-1747439999@prstats.preprodw.com SUMMARY:Model-Based Multivariate Analysis Of Abundance Data Using R (MBMVPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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.\n \n \n \n \n \n Intended Audiences\n 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). \n \n \n \n \n \n Course Details\n Last Up-Dated – 12:02:2021 \nDuration  – Approx. 30 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n Assumed quantitative knowledge\n An understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n \n \n \n \n \n Assumed computer background\n 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. \n \n \n \n \n \n Equipment and software requirements\n 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/. \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. \nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \nDownload R \nDownload RStudio \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 \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \nThere is a 20% cancellation fee to cover administration and possible bank fess. \nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \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\nDay 1 – approx. 3 hoursRevision of key “Stat 101” messages. \nDay 2 – approx. 3 hoursRevision of (univariate) regression analysis: the linear model\, generalised linear model.Main packages: lme4. \nDay 3 – approx. 3 hoursLinear mixed models\, the parametric bootstrap\, permutation tests and the bootstrap.Main packages: lme4\, mvabund. \nDay 4 – approx. 3 hoursModel selection\, classical multivariate analysis.Main packages: glmnet. \nDay 5 – approx. 3 hoursMultivariate abundance data: hierarchical models\, key properties\, hypothesis testing.Main packages: mvabund. \nDay 6 – approx. 3 hoursMultivariate abundance data: design-based inference for dependent data\, indicator species.Main packages: mvabund. \nDay 7 – approx. 3 hoursCompositional data\, explaining cross-species patterns using traits.Main packages: mvabund. \nDay 8 – approx. 3 hoursClassifying species based on environmental response\, predictive modelsMain packages: Speciesmix\, mvabund\, lme4. \nDay 9 – approx. 3 hoursModel-based ordination and inferenceMain packages: gllvm. \nDay 10 – approx. 3 hoursInferring interactions form co-occurrence dataMain packages: gllvm\, ecoCopula. \n\n\n\n \n \n \n \n \n Course Instructor\n \nProf. David Warton \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \nDavid 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. \nHis 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. URL:https://prstats.preprodw.com/course/model-based-multivariate-analysis-of-abundance-data-using-r-mbmvpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/09/16-Model-base-multivaraite-analysis-of-abundance-data-using-R-MBMV.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250514 DTSTAMP:20260419T050723 CREATED:20220222T020243Z LAST-MODIFIED:20230727T113547Z UID:10000317-1746403200-1747180799@prstats.preprodw.com SUMMARY:Introduction To Spatial Analysis Of Ecological Data Using R (ISPEPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \nBy the end of the course participants should: \n\nUnderstand the basic concepts of spatial data analysis\nKnow R’s spatial capabilities\nUnderstand how to import a range of spatial data sources into R\nBe confident with using R’s command-line interface (CLI) for spatial data processing\nBe 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)\nUnderstand coordinate reference systems (CRSs)\, be able to decide which CRS to use\, and how to reproject spatial data\nKnow how to visualize the results of a spatial analysis in the form of static and interactive maps\nHave the confidence to apply spatial analysis skills to their own projects\n\n \n \n \n \n \n Intended Audiences\n Academics and post-graduate students working on projects related to spatial data and want access to a powerful (geo)statistical and visualization programming language. \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. \nThe course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data. \n \n \n \n \n \n Course Details\n Last Up-Dated – 26:05:2021 \nDuration  – Approx. 24 hours \nECT’s – Equal to 2 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 there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \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. \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 \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\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 Day 1\n Approx. 4 HoursIntroduction to the courseKey concepts related to spatial dataR’s spatial ecosystemReading data from spatial file formatsUnderstanding R’s spatial classes \n \n \n \n \n \n Day 2\n Approx. 4 HoursCreating static and interactive maps:Customizing mapsMaking facet mapsCreating animationsUsing specific-purpose mapping packages \n \n \n \n \n \n Day 3\n Approx. 4 HoursAttribute data operations:Vector attribute subsetting\, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objects \n \n \n \n \n \n Day 4\n Approx. 4 HoursSpatial data operations:Spatial subsettingTopological relationsSpatial joiningAggregationMap algebraLocal\, focal\, and zonal raster operations \n \n \n \n \n \n Day 5\n Approx. 4 HoursGeometry operations:Geometric operations on vector dataGeometric operations on raster dataInteractions between rasters and vectors \n \n \n \n \n \n Day 6\n 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 \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/introduction-to-spatial-analysis-of-ecological-data-using-r-ispepr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2020/06/ISPE01-1.jpg GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250513 DTSTAMP:20260419T050723 CREATED:20220222T012351Z LAST-MODIFIED:20250513T221247Z UID:10000363-1746403200-1747094399@prstats.preprodw.com SUMMARY:Bayesian Hierarchical Modelling Using R (IBHMPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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.\n \n \n \n \n \n Course Details\n Last Up-Dated – 11:12:2020 \nDuration – Approx. 30 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of regression methods and generalised linear models. \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 \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\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 Day 1\n Approx 8 hours \nModule 1: Simple hierarchical regression modelsModule 2: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models \n \n \n \n \n \n Day 2\n Approx 8 hours \nModule 3: Simple hierarchical regression modelsModule 4: Hierarchical models for non-Gaussian dataPractical: Fitting hierarchical models \n \n \n \n \n \n Day 3\n Approx 8 hours \nModule 5: Hierarchical models vs mixed effects modelsModule 6: Multivariate and multi-layer hierarchical modelsPractical: Advanced examples of hierarchical models \n \n \n \n \n \n Day 4\n Approx 8 hours \nModule 7: Shrinkage and variable selectionModule 8: Hierarchical models and partial poolingPractical: Shrinkage modelling \n \n \n \n \n \n \n \n \n Dr. Andrew Parnell\n Works at - Hamilton Institute\, Maynooth University \n 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. \nResearch GateGoogle ScholarORCIDLinkedInGitHub \n \n \n \n \n \n \n \n \n \n \n \n \n Monday 21st\n 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. \n \n \n \n \n \n Teaches\n Stable Isotope MIxing Models Using R (SIMM) \nIntroduction to Bayesian Hierarchical Modelling (IBHM) \nTime Series Data Analysis Using R (TSDA) \nMissing Data Analytics Using R (MDAR) URL:https://prstats.preprodw.com/course/bayesian-hierarchical-modelling-using-r-ibhmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IBHM05R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20250505T000000 DTEND;TZID=Europe/London:20250509T000000 DTSTAMP:20260419T050723 CREATED:20241004T133429Z LAST-MODIFIED:20241004T133521Z UID:10000278-1746403200-1746748800@prstats.preprodw.com SUMMARY:Multivariate Analysis Of Ecological Communities Using R With The VEGAN Package (VGNRPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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\n Pre Recorded \n \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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”. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 08:10:2021 \nDuration – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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). \n \n \n \n \n \n Assumed computer background\n 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). \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 \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\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• Module 1: Introduction to community data analysis\, basics of programming \n• Module 2: Diversity analysis\, species-abundance distributions \n• Module 3: Distance and transformation measures \n• Module 4: Clustering and classification analysis \n• Module 5: Unconstrained ordinations: Principal Component Analysis \n• Module 6: Other unconstrained ordinations \n• Module 7: Constrained ordinations: RDA and other canonical analysis \n• Module 8: Statistical tests for multivariate data and variation partitioning \n• Module 9: Overview of Spatial analysis\, and recent Hierarchical Modeling of Species Communities (HMSC) methods \n \n \n \n \n \n \n \n Antoine Becker-Scarpitta\n Works at: CIRAD : CIRAD: The French agricultural research and international cooperation organization working for the sustainable development of tropical and Mediterranean regions. \n Teaches:\n\nMultivariate analysis of ecological communities in R with the VEGAN package (VGNR)\n\nAntoine 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. \nResearchGate \nGoogle Scholar \nORCID \nGitHub URL:https://prstats.preprodw.com/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnrpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/VGNR04R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250509 DTSTAMP:20260419T050723 CREATED:20220222T032735Z LAST-MODIFIED:20230727T122618Z UID:10000393-1746403200-1746748799@prstats.preprodw.com SUMMARY:Stable Isotope Mixing Models Using SIBER\, SIAR\, MixSIAR (SIMMPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 28:04:2023 \nDuration – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \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 \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\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 Day 1\n 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. \n \n \n \n \n \n Day 1\n Approx 8 hours \nBasic 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. \n \n \n \n \n \n Day 2\n Approx 8 hours \nUnderstanding 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. \n \n \n \n \n \n Day 3\n Approx 8 hours \nSIBER 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. \n \n \n \n \n \n Day 4\n Approx 8 hours \nAdvanced 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. \n \n \n \n \n \n \n \n \n Dr. Andrew Parnell\n Works at: Institute or University: Hamilton Institute\, Maynooth University \n 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. \nResearch GateGoogle ScholarORCIDLinkedInGitHub \n \n \n \n \n \n \n \n \n \n \n \n \n Monday 21st\n 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. \n \n \n \n \n \n Teaches\n Stable Isotope MIxing Models Using R (SIMM)\nIntroduction to Bayesian Hierarchical Modelling (IBHM)\nTime Series Data Analysis Using R (TSDA)\nMissing Data Analytics Using R (MDAR) URL:https://prstats.preprodw.com/course/stable-isotope-mixing-models-using-siber-siar-mixsiar-simmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SIMM08R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250508 DTSTAMP:20260419T050723 CREATED:20220222T032344Z LAST-MODIFIED:20230727T132810Z UID:10000392-1746403200-1746662399@prstats.preprodw.com SUMMARY:Introduction To Stan For Bayesian Data Analysis (ISBDPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 21.01.2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n Some experience and familiarity with R is required. No prior experience with Stan itself is required. \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 \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\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 Day 1\n Approx. 4 Hours \nTopic 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. \nTopic 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. \n \n \n \n \n \n Day 2\n Approx. 4 Hours \nTopic 2: Univariate models continued \nTopic 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. \n  \n \n \n \n \n \n Day 3\n Approx. 4 Hours \n\nTopic 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. \nTopic 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. \n\n  \n  \n \n \n \n \n \n \n Course Instructor\n \n\n\n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\n\n\n\nMark 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. URL:https://prstats.preprodw.com/course/introduction-to-stan-for-bayesian-data-analysis-isbdpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ISBD01R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050723 CREATED:20220222T033946Z LAST-MODIFIED:20230727T150725Z UID:10000395-1746403200-1746575999@prstats.preprodw.com SUMMARY:Making Beautiful And Effective Maps In R (MAPRPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 Course \n 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. \nBy the end of the course participants should: \n\nUnderstand the basic concepts behind the tmap package\nBe able to create a variety types of static maps\, including raster maps\, choropleth maps\, and point maps\nKnow how to create interactive maps and simple map applications using the shiny package\nBe able to create facet maps and map animations to represent spatiotemporal phenomenon\nKnow how to utilize  specific-purpose mapping packages to create cartograms or grid maps\nHave the confidence to apply map making skills to their own projects\n\n \n \n \n \n \n Intended Audiences\n 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 \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 to quickly create maps for their reports or websites \nThe course is designed for intermediate R users interested in maps making and R beginners who have prior experience with geographic data. \n \n \n \n \n \n Course Details\n Last Up-Dated – 23:02:23 \nDuration – Approx. 16 hours \nECT’s – Equal to 2 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.  \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 \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\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 \n \n \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 8 hoursIntroduction to mapping packages in RMaking static mapsApplying point\, lines\, polygons\, and raster map layersCustomizing mapsCreating interactive mapsSaving maps \n \n \n \n \n \n Day 2\n Approx. 8 hoursMaking facet mapsCreating animated mapsMaking inset mapsUsing specific-purpose mapping packagesCreating simple map applicationsOther mapping packages in R \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. \nResearchGateGoogleScholarORCIDLinkedInGitHub \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/making-beautiful-and-effective-maps-in-r-maprpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MAPR03R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050723 CREATED:20220222T021249Z LAST-MODIFIED:20230727T132359Z UID:10000386-1746403200-1746575999@prstats.preprodw.com SUMMARY:Bayesian Approaches To Regression And Mixed Effects Models Using R And brms (BARMPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 27:05:2021 \nDuration – 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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\nDay 1 – approx. 6 hours \nTopic 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. \nTopic 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. \nDay 2 – approx. 6 hours \nTopic 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. \nTopic 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. \n\n\n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar\n\nMark 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.\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/bayesian-approaches-to-regression-and-mixed-effects-models-using-r-and-brms-barmpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BARM01R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050723 CREATED:20220222T015650Z LAST-MODIFIED:20230920T132935Z UID:10000384-1746403200-1746575999@prstats.preprodw.com SUMMARY:Data Wrangling Using R And Rstudio (DWRSPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 Reocrded \n \n \n \n \n \n \n \n \n \n \n About This Course\n 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 22:04:2021 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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\nDay 1 \nApprox. 6 Hours \nTopic 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. \nTopic 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. \nDay 2 \nApprox. 6 Hours \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics. \nTopic 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. \nTopic 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. \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/data-wrangling-using-r-and-rstudio-dwrspr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DWRS02R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050723 CREATED:20220222T015141Z LAST-MODIFIED:20231222T133711Z UID:10000383-1746403200-1746575999@prstats.preprodw.com SUMMARY:Data visualization using GG plot 2 (R and Rstudio) (DVGGPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 08:04:2021 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThis 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. \n\n\nAlthough 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. \nAll 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. \n\n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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\nDay 1 \nApprox. 6 Hours \nTopic 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. \nTopic 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. \nTopic 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. \nTopic 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. \n  \nDay 2 \nApprox. 6 Hours \nTopic 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. \nTopic 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. \nTopic 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. \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website URL:https://prstats.preprodw.com/course/data-visualization-using-gg-plot-2-r-and-rstudio-dvggpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DVGG02.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050723 CREATED:20220222T014709Z LAST-MODIFIED:20230323T215852Z UID:10000382-1746403200-1746575999@prstats.preprodw.com SUMMARY:Introduction To Statistics Using R And Rstudio (IRRS03R) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 18:03:2021 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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 Day 1\n Approx. 6 Hours \nTopic 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. \nTopic 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. \nTopic 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. \n \n \n \n \n \n Day 2\n Approx. 6 Hours \nTopic 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. \nTopic 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. \nTopic 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. \nTopic 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. \n  \n \n \n \n \n \n \n Course Instructor\n \n\n\n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\n\n\n\nMark 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. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/introduction-to-statistics-using-r-and-rstudio-irrs03r/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IRRS03R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250507 DTSTAMP:20260419T050724 CREATED:20220221T210549Z LAST-MODIFIED:20230727T123238Z UID:10000357-1746403200-1746575999@prstats.preprodw.com SUMMARY:Introduction to Python and Programming in Python (PYINPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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\n Pre Recorded \n \n \n \n \n \n \n \n \n \n \n \n About this course\n \nPython 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. \n\n \n \n \n \n \n Intended Audiences\n \nThis 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. \n\n \n \n \n \n \n Course Details\n Last Up-Dated – 21:05:2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \n \n \n \n \n \n Assumed quantitative knowledge\n \nNo particular knowledge of mathematics or statistics is required. \n\n \n \n \n \n \n Assumed computer background\n \nNo 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. \n\n \n \n \n \n \n Equipment and software requirements\n \nAttendees 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. \n\nIf 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.\n \n \n \n \n \n \n \n \n \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\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 \n COURSE PROGRAMME\n \n \n \n \n \n \n \n \n \n \n Day 1\n Approx. 6 Hours \nTopic 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. \nTopic 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. \nTopic 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. \n \n \n \n \n \n Day 2\n Approx. 6 Hours \nTopic 4: Programming II. Having covered iterations\, we now turn to other major programming features in Python\, specifically\, conditionals\, functions\, and exceptions. \nTopic 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. \nTopic 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. \n \n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website \n\n\nResearchGate \nGoogle Scholar \nMark 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. URL:https://prstats.preprodw.com/course/introduction-to-python-and-programming-in-python-pyinpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250505 DTEND;VALUE=DATE:20250506 DTSTAMP:20260419T050724 CREATED:20220222T020843Z LAST-MODIFIED:20230727T125110Z UID:10000385-1746403200-1746489599@prstats.preprodw.com SUMMARY:Introduction / Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDAPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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. \n \n \n \n \n \n Intended Audiences\n 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. \n \n \n \n \n \n Course Details\n Last Up-Dated – 20:05:2022 \nDuration – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \nAlthough 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. \nAll 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. \n \n \n \n \n \n Assumed quantitative knowledge\n 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. \n \n \n \n \n \n Assumed computer background\n 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. \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 \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\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\nDay 1 \nApprox. 6 hours \nTopic 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. \nTopic 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. \nTopic 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. \nDay 2 \nApprox. 6 hours \nTopic 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. \nTopic 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. \nTopic 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. \n\n\n\n \n \n \n \n \n Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar\n\nMark 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.\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map URL:https://prstats.preprodw.com/course/introduction-fundamentals-of-bayesian-data-analysis-statistics-using-r-fbdapr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/FBDA01R.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20250207 DTEND;VALUE=DATE:20250208 DTSTAMP:20260419T050724 CREATED:20220504T113357Z LAST-MODIFIED:20240130T173931Z UID:10000409-1738886400-1738972799@prstats.preprodw.com SUMMARY:Introduction to eco-phylogenetics and comparative analyses using R (ECPHPR) DESCRIPTION:Recorded\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses.\n \n \n \n \n \n Course Details\n Last Up-Dated – 11:02:2021 \nDuration – Approx. 30 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will\noccur between (UK local time) at:\n• 8:00am-10:00am\n• 11:00pm-13:00pm\n• 14:30pm-16:30pm \nAll sessions will be video recorded and made available to all attendees.\n \n \n \n \n \n Assumed quantative knowledge\n 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.\n \n \n \n \n \n Assumed computer background\n 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).\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 \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 \n• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists\, phylogenetic inference (quick overview)\, phylogenies aevolutionary hypotheses. \n• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning\, resolving polytomies\, sticking species). Visualizing large phylogenies. \n• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom\, V.PhyloMaker\, SUNPLIN and randtip R package. \n \n \n \n \n \n \n Day 2\n Approx. 7 Hours \n• Introduction to the eco-phylogenetic framework\, classical conception and posterior modifications. \n• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices\, null models\, applications to biodiversity conservation. \n• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity.\n \n \n \n \n \n Day 3\n Approx. 7 Hours \n• Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses. \n• Spatial phylogenetics. RPD\, RPE and CANEPE analysis. \n• Overview of functional trait ecology. Functional richness\, evenness and divergence.Community weighted means. \n• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology\n \n \n \n \n \n Day 4\n Approx. 7 Hours \n\nThe phylogenetic comparative method\, from independent contrasts to sophisticated modelling.\nAnalyses of phylogenetic signal and models of evolution: rationale\, common- practice\, and new trends.\nCorrelated evolution and ancestral trait reconstruction.\nAnalyses of diversification\, speciation and extinction rates in a geographic context.\n\n\n \n \n \n \n \n Day 5\n Approx. 7 Hours \n\nThe need to account for phylogenetic relationships in models.\nMost common phylogenetic modelling approaches: PGLS\, PGLMM\, BayesianPMM.\nPutting phylogenies in the geography: how to combine phylogenies with species distribution models.\n\n \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \n \n \n \n \n Rafael Molina Venegas \nWorks at: Universidad Autónoma de Madrid \nTeaches: Introduction to eco-phylogenetics and comparative analyses using R \nThe 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. \nVisit Website \nGoogle Scholar URL:https://prstats.preprodw.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecphpr/ LOCATION:Recorded\, United Kingdom CATEGORIES:Previously Recorded Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png GEO:55.378051;-3.435973 END:VEVENT END:VCALENDAR