zhenkewu

baker

👩‍🍳 🥧 Bayesian Analysis Kit for Etiology Research via Nested Partially Latent Class Models

zhenkewu.github.io

Zhenke Wu, PhD Research Website

bugs.models

.bug files for use in WinBUGS or OpenBUGS model fitting

nplcm

R package for fitting nested partially latent class models (nplcm)

baker_example

Example Code for 'baker' Package

demo_code

Demonstration code for small problems

spotgear

🔭 Subset Profiling and Organizing Tools for Gel Electrophoresis Autoradiography in R

ddtlcm

Tree-regularized latent class models to improve estimation under weak separation and small sample sizes

doubletree

🎄🎄 Nested Latent Class Models for Domain-Adaptive and Semi-supervised Learning of Cause-of-Deaths using Verbal Autopsy

lotR

🌲 Integrating Sample Similarity Information into Latent Class Models: A Tree-Structured Shrinkage Approach

rewind

⏪ R package for: Reconstructing Etiology with Binary Decomposition

adv-r

Advanced R programming: a book

CUSUM-RL

Implementation of "Reinforcement Learning in Possibly Nonstationary Environments"

ddtlcm_shiny

genAI

learning and implementing generative AI tools

LCVA

Nested latent class model for Verbal Autopsy

MediationRL

Implementation of "A Reinforcement Learning Framework for Dynamic Mediation Analysis" (ICML 2023) in Python.

mpcr

R package for estimating treatment effects in matched-pair cluster randomized trials (MPCR) using covariate calibration

MRT

Micro randomized trial with peer effects

ormachine

r-pkgs

Building R packages

RxTree

scalable-bayesian-inference-papers

slamR

Fast Algorithms for Fitting Structured Latent Attribute Models (SLAM) in R

slvm_va

build and test structured latent variable models for verbal autopsy data that do regression, scale to large data sizes and work in the absence of gold-standard data

ultrametricMat

The ultrametricMat is a package designed to conduct the Bayesian inference on the ultrametric matrices by the leveraging the bijection map between the ultrametric matrices and the tree space. We refer to more details to Yao et al. (2023+) Geometry-driven Bayesian Inference for Ultrametric Covariance Matrices.