duolajiang

Active-Learning-Bayesian-Convolutional-Neural-Networks

Active Learning on Image Data using Bayesian ConvNets

AdversarialNetsPapers

The classical papers and codes about generative adversarial nets

aetros-cli

AETROS SDK. Python library to manage/monitor deep learning training in AETROS Trainer

bayesian-belief-networks

Pythonic Bayesian Belief Network Package, supporting creation of and exact inference on Bayesian Belief Networks specified as pure python functions.

bayesian-neural-network-mnist

Bayesian neural network using Pyro and PyTorch on MNIST dataset

BDA_py_demos

Bayesian Data Analysis demos for Python

BDA_R_demos

Bayesian Data Analysis demos for R

blkvar

Package for estimating impacts and cross-site variation in multi-site or blocked RCTs

ExtendingInferences

Code for `Extending inferences from a randomized trial to a new target population'

RCTrep

duolajiang/RCTrep

duolajiang/ExtendingInferences

Code for `Extending inferences from a randomized trial to a new target population'

duolajiang/bayesian-neural-network-mnist

Bayesian neural network using Pyro and PyTorch on MNIST dataset

duolajiang/BDA_py_demos

Bayesian Data Analysis demos for Python

duolajiang/BDA_R_demos

Bayesian Data Analysis demos for R

duolajiang/blkvar

Package for estimating impacts and cross-site variation in multi-site or blocked RCTs

duolajiang/CausalInference

CRAN Task View: Causal Inference

duolajiang/contextual

Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies

duolajiang/denovo

Lee, Small and Dominici (2020) "Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies"

duolajiang/dgrtwo.github.com

My website

duolajiang/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

duolajiang/empirical-bayes-book

Introduction to Empirical Bayes: Examples from Baseball Statistics

duolajiang/first

haha

duolajiang/footstrap

Tutorial package

duolajiang/guidelines

Guidelines for publishing a repository in the IKNL space

duolajiang/interpretable-ml-book

Book about interpretable machine learning

duolajiang/IntpTrtRule

duolajiang/Making-accurate-and-interpretable-decisions-for-binary-outcome

duolajiang/Making-accurate-and-interpretable-old-decisions-for-binary-outcome-old

duolajiang/markdown-here

Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

duolajiang/mlxtend

A library of extension and helper modules for Python's data analysis and machine learning libraries.

duolajiang/OWRCT_codes_package

Codebase for OW-RCT paper

duolajiang/possum

POSterior SUMmarization

duolajiang/pymatch

duolajiang/python-causality-handbook

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.

duolajiang/RAOS-Examples

Regression and other stories R examples

duolajiang/sbw

:exclamation: This is a read-only mirror of the CRAN R package repository. sbw — Stable Balancing Weights for Causal Inference and Estimation with Incomplete Outcome Data

duolajiang/TukeySens

R package for sensitivity analysis method in causal inference based on Tukey's Factorization

duolajiang/two-step-framework-causal-effects-validation-simulation

duolajiang/vantage_causalEvaluation