causal.decomp - Causal Decomposition Analysis

We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).

Last updated 2 years ago

2.00 score 4 scripts 209 downloads

rrMixture - Reduced-Rank Mixture Models

We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Last updated 3 years ago

openblascpp

2.00 score 5 scripts 177 downloads

MixSemiRob - Mixture Models: Parametric, Semiparametric, and Robust

Various functions are provided to estimate parametric mixture models (with Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric mixture models. The package performs hypothesis tests and addresses label switching issues in mixture models. The package also allows for parameter estimation in mixture of regressions, proportion-varying mixture of regressions, and robust mixture of regressions.

Last updated 1 years ago

1.00 score 152 downloads