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Zhenke Wu's Projects

adv-r icon adv-r

Advanced R programming: a book

baker icon baker

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

bugs.models icon bugs.models

.bug files for use in WinBUGS or OpenBUGS model fitting

cusum-rl icon cusum-rl

Implementation of "Reinforcement Learning in Possibly Nonstationary Environments"

ddtlcm icon ddtlcm

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

doubletree icon doubletree

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

genai icon genai

learning and implementing generative AI tools

lcva icon lcva

Nested latent class model for Verbal Autopsy

lotr icon lotr

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

mediationrl icon mediationrl

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

mpcr icon mpcr

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

mrt icon mrt

Micro randomized trial with peer effects

nplcm icon nplcm

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

rewind icon rewind

⏪ R package for: Reconstructing Etiology with Binary Decomposition

slamr icon slamr

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

slvm_va icon 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

spotgear icon spotgear

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

ultrametricmat icon 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.

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