Discover mark-andrews/iglm02 Open Source project

Introduction to statistics using R and Rstudio

irrs03

immr05

Introduction to Multilevel (hierarchical, or mixed effects) Models

bayeslund2017

LaTeX source of slides for a talk given at Bayes@Lund 2017

Slides, code, etc for a workshop introducing R

introduction-to-R

immr03

Introduction to Multilevel (hierarchical, or mixed effects) Models

introduction-to-git

Introduction to Git

Tools to help teach data analysis using R to NTU Psychology students

psyntur

immr04

Introduction to Multilevel (hierarchical, or mixed effects) Models

teaching-bayes-talmo24

Slides etc for a talk on teaching Bayesian methods

Some code and data for a workshop on posterior predictive checks

bayesian_ppc

guardian-league-table-2018

Shiny/R app for viewing the Guardian university league tables

teaching_with_R_lancaster

A presentation for a meeting about teaching R in psychology departments

Introduction to Bayesian data analysis

wlv-bayes-2022

sips2019

Materials for a workshop on reproducible research at SIPS2019

A four day course on statistics and data science for the British Antarctic Survey

bas2021datascience

psypag-kent-2017

Slides and code for a talk given at PsyPAG, University of Kent, July 2017

Workshop on Bayesian Data Analysis in University of Lisbon, January 2024

bayes-workshop-ulisboa-2024

imdl03

Introduction to Machine Learning and Deep Learning using R

iglm04

Introduction to Generalized Linear Models

bayes-lmu-2018

Slides, code, etc for a workshop on Bayesian data analysis held at LMU, September 2018

ntupsystats-misc

Miscellaneous scripts, code, or data files used during statistics teaching in Psychology Department in Nottingham Trent University

Introduction to statistics using R and Rstudio

bada01

Bayesian Data Analysis

irrs01

pyml02

Introduction to Machine Learning and Deep Learning using Python

Workshop on multilevel/mixed-effects models

immr06

Bayesian Approaches to Regression and Mixed Effects Models using R and brms

barm01

Introduction to Python for Data Science

intropy4ds

Introduction to Generalized Linear Models

iglm03

msms02

Model selection and model simplification

Teaching statistics with R in Psychology Symposium

teachstatsr-jun22

Introduction to Python and Programming in Python

pyin02

Introduction to Stan for Bayesian Data Analysis

isbd01

irrs_ntu_doctoral_school

Info and materials for a one day training course on R and RStudio

R and Rmd scripts, etc, for psyc30815 (2020-21)

psyc30815-202021

immr02

Introduction to Multilevel (hierarchical, or mixed effects) Models

fbda01

The Fundamentals of Bayesian Data Analysis

RMarkdown source for the "Introduction to Statistics and Data Science Using R" textbook

isdsr-book

methods-hub-2023-talk

dwdv01

Introduction to Data Wrangling and Data Visualization using R

rsdwr

Data wrangling and RMarkdown-based reproducible reports using R

gamr01

Nonlinear Regression using Generalized Additive Models

bnctools-project

Python etc tools for working with the British National Corpus

Python

intro-to-brms

A gentle and brief introduction to brms

msms03

Workshop on model selection and evaluation

Nonlinear regression using R: Generalized Linear Models, Generalized Additive Models, Basis function regression, Gaussian processes, and other methods.

gnam02

pyin03

Introduction to Python

dvgg01

Introduction to Data Visualization using R

dwrs01

Introduction to Data Wrangling using R

msms01

Model selection and model simplification

Slides etc accompanying a spoken presentation at BPS annual conference, May 2, 2018.

bpsconf2018

Some notebook etc for a workshop on Python

pspy01

A demo reproducible data analysis project

repdemoproj

intro_bda_qub

Introduction to Bayesian Data Analysis

iglm01

Introduction to Generalized Linear Models

dwrs02

Introduction to Data Wrangling using R

project101

A demo repository for teaching and training purposes only

gnam01

RMarkdown, slides, data, etc for a workshop on generalized linear and generalized additive models

psbayes

Slides, code, etc for a workshop on Bayesian data analysis (psstatistics, December 2018)

Demo of how to calculate class conditional probabilities using logistic regression

cheater-profile

BayesianAccountMemoryText

Code, data, manuscript related to a research project on a Bayesian account of memory for text

Slides and RMarkdown files for an Introduction to R workshop

intro2brms

Introduction to brms

bps-introduction-to-R

Two day course on Introduction to Machine Learning and Deep Learning using R

imdl01

qsort-shiny-app

A Q-sort app

rdrp01

Reproducible Data Science using RMarkdown, Git, R packages, Docker, Make, and other tools

imdl02

Introduction to Machine Learning and Deep Learning using R

dvgg02

Introduction to Data Visualization using R

Python tools for the management of the psyc20255 module at Nottingham Trent University.

psyc20255managementproject

Python

intro2pystats

Introduction to data analysis and statistics using Python

Introduction to Machine Learning and Deep Learning using Python

pyml01

A workshop on Bayesian methods at Lancaster University, June 2018

lancaster-bayes-workshop

Materials for course on R and RStudio

ntu-doctoral-rrstudio

share_rmd_html_privately

A demo of a password protected html page in GitHub pages

immr23

Introduction to Multilevel, Hierarchical, or Mixed Effects Models using R

tilt_roundtable

Slides for event on writing textbooks

R/RStudio project for mybinder

psyntur_in_binder

ljmu_multilevel_models

Training course on multilevel models

research-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 Python

Slides on using HDDM to analyse ANS

bps-cogdev19-ans

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, 2017

esslli-2017-bayesian-methods