My GitHub: teazrq
Dimension Reduction Forests (R package drforest)
This package provides the statistical estimation methods for dimension reduction forests and local subspace variable importance.
Local subspace variable importance can be used for detecting influential variables in a personalized prediction/recommendation.
Random Forests for Heterogeneous Treatment Effect Estimation with Multiple Responses (R package MOTE.RF)
Designed for a setting in which the covariates X and multivariate response Y are both measured before and after the intervention
Applied to a microbiome study to estimate the dietary effect on multiple health outcomes
A Nonnegative Matrix Factorization Tool Box (R package MatrixFact)
This package implements a variety of matrix factorization tools, such as NMF, ONMF, semi-NMF, semi-orthogonal NMF, and some of their extensions to binary data.
Our method semi-orthogonal NMF can be applied to document-word matrix to extract meaningful information for analyzing text data.
Orthogonality Constrained Optimization and Dimension Reduction (R package orthoDr)
This package offers an optimization solver for orthogonality constrained problems: min f(X) s.t. XTX = I by utilizing the algorithm developed by Wen & Yin (2013). We utilize this to solve a variety of semiparametric dimension reduction and personalized medicine problems.
Reinforcement Learning Trees (R package RLT)
On CRAN Task View: Machine Learning & Statistical Learning
You can find this example file which further explains the functions.
The survival analysis component is already implemented. However, some features are still under experiment.
Recursively Imputed Survival Trees (RIST)
Temporary R-code: RIST Example & ReadMe
R Package: will be integrated with RLT (still working on it...)