- MCMC Algorithms: a directory of MCMC algorithms, including some quite obscure ones, nicely referenced.
- Stan best tips and tricks
- Also, look for pull requests made by Bob Carpenter (would be good to compile a list of tips from these)
- Michael Betancourt
- Model selection tutorials and talks
- Typical Sets and the Curse of Dimensionality
- Diagnosing Biased Inference with Divergences
- Bayesian networks
bnlearn - Posterior z-score against contraction plots
- Reproducing kernel Hilbert spaces in Machine Learning
- From Zero to Reproducing Kernel Hilbert Spaces in Twelve Pages or Less
- Kernel cookbook
- Applied Causal Analysis (with R)
- Introduction to Causal Inference
- Causal Inference Primer
- Causal Inference: The Mixtape
- Program Evaluation for Public Service
- Literature on Recent Advances in Applied Micro Methods
- Evaluating Social Programs Webinar Series (J-PAL)
- Simultaneous equations model
- Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
- Geographic Data Science
- Malaria ATLAS podcast
- Cancer Atlas guide
- Spatial data
- Spatial Data Science with R: and the book
- Spatial and spatio-temporal statistics
- Forecasting: Principles and Practise
- Gaussian random fields and correlation functions
- Modeling Dependent Data: An Excursion
- Gaussian Process Workshop on Spatio-Temporal Modelling, 2014
- Understanding Persistence, Morgan Kelly
- The technical vignettes of Christopher Paciorek: 1, 2, 3, 4, 5.
- Forecast Scoring and Calibration, Advanced Topics in Statistical Learning, Ryan Tibshirani: some lecture notes on forecasting, mostly drawing from the work of Tilmann Gneiting.
- Ben Bolker's GLMM FAQ: from the author of
lme4. - Ben Bolker's introduction to GLMMs in the context of ecological data: Maybe a bit old, but still useful.
- Aki Vehtari's cross-validation FAQ: great resource about cross-validation and model comparison more broadly. Helps to see the connections between model comparison techniques and make things feel less arbitrary.
- Telling Stories with Data
- Foundations of Data Science
- GAMs in R
- João Neto's Markdowns
- Ergodic theory
- Stein’s Paradox
- Statistical theory
- Surrogates: Gaussian process modeling, design and optimization for the applied sciences: a book about Gaussian process emulators, with R code.
- Patterns, Predictions, and Actions
- Deep learning
- Algorithmic Foundations of Learning
- Bayesian Methods in Machine Learning
- Generative Models
- Interpretable Machine Learning
- Graph Representation Learning
- Advanced Topics in Statistical Machine Learning
- Pen & Paper: Exercises in Machine Learning: For when I fantasise about undergrad and think that I have more time than I do
- Andrew Gelman
- God is in every leaf of every tree: resonates with my experience doing applied statistics
- Christian Robert
- Dan Simpson
- Thomas Lumley
- Jim Savage
- Population health exchange
- Gwern
- Model, Inference and Algorithms Meeting at the Broad Institute
- Gaussian Process Seminar Series
- MRC Biostatistics Unit, University of Cambridge
- ISBA - International Society of Bayesian Analysis
- DeepMind ELLIS UCL CSML Seminar Series
- OATML research group
- Rohan Alexander
- IQSS at Harvard University
- Ten Statistical Ideas that Changed the World (Hastie and Tibshirani)
- R Packages
- R package primer
- When in Doubt, Try to Upgrade Your Software Packages
- The R Inferno
- Advanced R: Debugging
- A way to run code before all tests in
testthat - Ways to reuse
roxygen2documentation - (Much) Faster Package (Re-)Installation via Caching: use
ccacheto rebuild compiled packages where the source code is unchanged faster - The
magickpackage: Advanced Image-Processing in R - The
hunspellpackage: High-Performance Stemmer, Tokenizer, and Spell Checker for R: I've found it useful to systematically check written documents in RMarkdown for spelling errors using this package. stylerandlintrfor code linting
R-INLAsandbox of Jeff Eaton- International Virtual Workshop "Introducting R-INLA and Its Applications"
- Tutorial 3: Bayesian Computing with INLA -- Håvard Rue
- A Primer on Crashing INLA Models
- The
groupoption, slides by Riebler - Random slopes models
- Statistical Rethinking in
R-INLA: a translation of the Statistical Rethinking book homeworks intoR-INLA(andtidyverse)
- The comprehensive TMB documentation
- Code snippets
- Guidelines for including
TMBC++ code in an R package TMBtools: the package to use for easily developing an R package which includes compiledTMBcode.- Troubleshooting with
glmmTMB: many of these troubleshooting tips are applicable not only toglmmTMBput toTMBmore generally.
- The missing semester of your CS education
- Bash reference manual
- CS50's Introduction to Computer Science
- Algorithmic Thinking: all are based on LeetCode problems
- Write of Passage
- On Writing Well
- Trees, maps, and theorems: book on clear and effective scientific communication.
- The Chartmaker Directory
- Using LaTeX plus R plus tikzDevice
- Graphics principles
- Force the origin to start at 0
- BibTeX tidy: this webapp will tidy your
.bibfile for you, including finding duplicates.
- Uploading a paper to arXiv.org: note here that Trevor Campbell has a section on his website for "common questions advisees" have. This seems like a very good idea to me.
- Managing to Change the World: The Nonprofit Manager's Guide to Getting Results by Alison Green and Jerry Hauser
- The Effective Manager by Mark Horstman
- How to manage up using these delegation tips
- GTD in 15 minutes – A Pragmatic Guide to Getting Things Done
- Focusmate
- Ultraworking
- How to Increase Productivity: The Ultimate Psychological guide
- Mental Models: The Best Way to Make Intelligent Decisions
- Mental models
- The Complete Guide to Deep Work
- Zen Habits
- Every Noise at Once: can be used to, among other things, browse algorithmically generated playlists for thousands of genres. For example, here is one for the "Sound of Drumfunk". There are also introductory, current pulse and leading edge versions of each playlist.