An R package for generalised survival models

dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.

An R package for modeling asymmetric spatial associations between cell types in tissue images using a multilevel Bayesian framework.

Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes

Fast algorithms for fitting topic models and non-negative matrix factorizations to count data.

A tidy API for graph manipulation

Process 1000G phase3 data as a reference for various populations.

R package for performing 2-sample MR using IEU OpenGWAS database

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

R package for "sum of single effects" regression.

HistogramZoo - A set of methods for histogram segmentation and characterization

🍃 SimplePlus - A minimalist and clean LaTeX Beamer theme

R package for CAUSE

R package for Genetic Factor Analysis (GFA)

Anki is a smart spaced repetition flashcard program

Optimizing NOTEARS Objectives via Topological Swaps

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization

R-package for structural equation modeling based on GWAS summary data

A faster and angrier package for EBMF.

Empirical Shrinkage Mendelian Randomization

Simulate GWAS data from an arbitrary DAG