Alternatives and similar repositories for modern-bayes:

Users that are interested in modern-bayes are comparing it to the libraries listed below

• Chrisejorge / Causal-Inference
  Materials Collection for Causal Inference
  ☆45Updated 2 years ago
• stablemarkets / intro_bayesian_causal
  Repository for Introduction to Bayesian Estimation of Causal Effects
  ☆61Updated 4 years ago
• zhangry868 / Bayesian-Statistics
  Course material for Bayesian and Modern Statistics, STA601, Duke University, Spring 2015.
  ☆20Updated 9 years ago
• resteorts / data-mine
  ☆18Updated 5 years ago
• cliburn / sta-663-2021
  Computational Statistics and Statistical Computing
  ☆37Updated 4 years ago
• mmarouen / The-Elements-Of-Statistical-Learning
  This repository contains R code for exercices and plots in the famous book.
  ☆46Updated last year
• ehkennedy / npcausal
  ☆94Updated last year
• FJRubio67 / StatisticalInference
  Lecture Notes on Statistical Inference
  ☆76Updated 6 months ago
• xuanyin / causal-mediation-analysis-for-ab-tests
  The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis
  ☆23Updated 2 years ago
• jayelm / hoff-bayesian-statistics
  R Markdown notes for Peter D. Hoff, "A First Course in Bayesian Statistical Methods"
  ☆129Updated 5 years ago
• keihirano / modern-sampling
  Course Materials for AEA Short Course on Modern Sampling Methods
  ☆29Updated 3 years ago
• mattiasvillani / BayesLearnCourse
  Bayesian Learning course at Stockholm University
  ☆151Updated 10 months ago
• mattiasvillani / AdvBayesLearnCourse
  Course material for the PhD course in Advanced Bayesian Learning
  ☆59Updated 2 months ago
• MCKnaus / dmlmt
  Double Machine Learning for Multiple Treatments
  ☆40Updated 4 years ago
• vsemenova / orthoml
  Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Seme…
  ☆25Updated last year
• MCKnaus / causalDML
  Implementation of Double Machine Learning
  ☆35Updated 2 weeks ago
• gsbDBI / ds-wgan
  Design of Simulations using WGAN
  ☆50Updated 2 years ago
• pzivich / Delicatessen
  Delicatessen: the Python one-stop sandwich (variance) shop 🥪
  ☆27Updated 2 weeks ago
• robjhyndman / fpp3_slides
  Slides to accompany Forecasting: Principles and Practice, 3rd edition
  ☆73Updated last year
• stablemarkets / BayesianTutorials
  Implementing MCMC sampling from scratch in R for various Bayesian models
  ☆109Updated last year
• soerenkuenzel / causalToolbox
  ☆42Updated 4 years ago
• NickCH-K / causalbook
  Replication code and downloadable example data sets for The Effect
  ☆196Updated 4 years ago
• kjytay / stanford-stats
  My notes from class
  ☆66Updated 7 years ago
• saberpowers / causalLearning
  Methods for heterogeneous treatment effect estimation
  ☆24Updated 3 years ago
• anishazaveri / austen_plots
  Demo data and code for "Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding".
  ☆26Updated 3 years ago
• lihualei71 / cfcausal
  R package cfcausal
  ☆27Updated 2 years ago
• swager / balanceHD
  Approximately balanced estimation of average treatment effects in high dimensions.
  ☆34Updated 3 years ago
• CausalInference / pygformula
  The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-va…
  ☆27Updated last month
• ASKurz / Working-through-Regression-and-other-stories
  Working through "Regression and other stories," one chapter at a time
  ☆55Updated last year
• rizbicki / FlexCoDE
  ☆32Updated 3 years ago