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Source code for the book "Doing Data Science in R" by Mark Andrews (SAGE publishers, 2021)gesis-48-bayesian-multilevel
Bayesian multilevel models course for Gesis 48. March 2019irrs03
Introduction to statistics using R and Rstudioimmr05
Introduction to Multilevel (hierarchical, or mixed effects) Modelsbayeslund2017
LaTeX source of slides for a talk given at Bayes@Lund 2017introduction-to-R
Slides, code, etc for a workshop introducing Rimmr03
Introduction to Multilevel (hierarchical, or mixed effects) Modelsintroduction-to-git
Introduction to Gitpsyntur
Tools to help teach data analysis using R to NTU Psychology studentsimmr04
Introduction to Multilevel (hierarchical, or mixed effects) Modelsteaching-bayes-talmo24
Slides etc for a talk on teaching Bayesian methodsbayesian_ppc
Some code and data for a workshop on posterior predictive checksguardian-league-table-2018
Shiny/R app for viewing the Guardian university league tablesteaching_with_R_lancaster
A presentation for a meeting about teaching R in psychology departmentswlv-bayes-2022
Introduction to Bayesian data analysissips2019
Materials for a workshop on reproducible research at SIPS2019bas2021datascience
A four day course on statistics and data science for the British Antarctic Surveyiglm02
Introduction to Generalized Linear Modelspsypag-kent-2017
Slides and code for a talk given at PsyPAG, University of Kent, July 2017bayes-workshop-ulisboa-2024
Workshop on Bayesian Data Analysis in University of Lisbon, January 2024imdl03
Introduction to Machine Learning and Deep Learning using Riglm04
Introduction to Generalized Linear Modelsbayes-lmu-2018
Slides, code, etc for a workshop on Bayesian data analysis held at LMU, September 2018ntupsystats-misc
Miscellaneous scripts, code, or data files used during statistics teaching in Psychology Department in Nottingham Trent Universitybada01
Bayesian Data Analysisirrs01
Introduction to statistics using R and Rstudiopyml02
Introduction to Machine Learning and Deep Learning using Pythonimmr06
Workshop on multilevel/mixed-effects modelsbarm01
Bayesian Approaches to Regression and Mixed Effects Models using R and brmsintropy4ds
Introduction to Python for Data Scienceiglm03
Introduction to Generalized Linear Modelsmsms02
Model selection and model simplificationteachstatsr-jun22
Teaching statistics with R in Psychology Symposiumpyin02
Introduction to Python and Programming in Pythonisbd01
Introduction to Stan for Bayesian Data Analysisirrs_ntu_doctoral_school
Info and materials for a one day training course on R and RStudiopsyc30815-202021
R and Rmd scripts, etc, for psyc30815 (2020-21)immr02
Introduction to Multilevel (hierarchical, or mixed effects) Modelsfbda01
The Fundamentals of Bayesian Data Analysisisdsr-book
RMarkdown source for the "Introduction to Statistics and Data Science Using R" textbookmethods-hub-2023-talk
methods-hub-2023-talkdwdv01
Introduction to Data Wrangling and Data Visualization using Rrsdwr
Data wrangling and RMarkdown-based reproducible reports using Rgamr01
Nonlinear Regression using Generalized Additive Modelsbnctools-project
Python etc tools for working with the British National Corpusintro-to-brms
A gentle and brief introduction to brmsmsms03
Workshop on model selection and evaluationgnam02
Nonlinear regression using R: Generalized Linear Models, Generalized Additive Models, Basis function regression, Gaussian processes, and other methods.pyin03
Introduction to Pythondvgg01
Introduction to Data Visualization using Rdwrs01
Introduction to Data Wrangling using Rmsms01
Model selection and model simplificationbpsconf2018
Slides etc accompanying a spoken presentation at BPS annual conference, May 2, 2018.pspy01
Some notebook etc for a workshop on Pythonrepdemoproj
A demo reproducible data analysis projectintro_bda_qub
Introduction to Bayesian Data Analysisiglm01
Introduction to Generalized Linear Modelsdwrs02
Introduction to Data Wrangling using Rproject101
A demo repository for teaching and training purposes onlygnam01
RMarkdown, slides, data, etc for a workshop on generalized linear and generalized additive modelspsbayes
Slides, code, etc for a workshop on Bayesian data analysis (psstatistics, December 2018)cheater-profile
Demo of how to calculate class conditional probabilities using logistic regressionBayesianAccountMemoryText
Code, data, manuscript related to a research project on a Bayesian account of memory for textintro2brms
Introduction to brmsbps-introduction-to-R
Slides and RMarkdown files for an Introduction to R workshopimdl01
Two day course on Introduction to Machine Learning and Deep Learning using Rqsort-shiny-app
A Q-sort appimdl02
Introduction to Machine Learning and Deep Learning using Rdvgg02
Introduction to Data Visualization using Rpsyc20255managementproject
Python tools for the management of the psyc20255 module at Nottingham Trent University.intro2pystats
Introduction to data analysis and statistics using Pythonpyml01
Introduction to Machine Learning and Deep Learning using Pythonlancaster-bayes-workshop
A workshop on Bayesian methods at Lancaster University, June 2018ntu-doctoral-rrstudio
Materials for course on R and RStudioshare_rmd_html_privately
A demo of a password protected html page in GitHub pagesimmr23
Introduction to Multilevel, Hierarchical, or Mixed Effects Models using Rtilt_roundtable
Slides for event on writing textbookspsyntur_in_binder
R/RStudio project for mybinderljmu_multilevel_models
Training course on multilevel modelsresearch-integrity-2017
Slides for my talk entitled *Open, transparent, and reproducible research with RMarkdown, Jupyter notebooks, and Git* delivered at the Improving research integrity and reproducibility through openness and transparency symposium held in UCC, Oct 23, 2017.pysc01
Introduction to Scientific, Numerical, and Data Analysis Programming in Pythonbps-cogdev19-ans
Slides on using HDDM to analyse ANSesslli-2017-bayesian-methods
Materials for Bayesian Methods for Language Sciences, a course taught at 29th European Summer School in Logic, Language, and Information, University of Toulouse (France), 17-28 July, 2017Love Open Source and this site? Check out how you can help us