Software

Computational packages

*Master student

1. Tsao, H.-S.* and Chen, L.-P. (2024). R package "GUEST" for Graphical models in ultrahigh-dimensional and error-prone data via boosting algorithm. Link: https://cran.r-project.org/web/packages/GUEST/index.html

2. Chen, L.-P. and Yi, G. Y. (2023). R package "AteMeVs" for Average treatment effects with measurement error and variable selection for confounders. Link: https://cran.r-project.org/web/packages/AteMeVs/index.html

3. Huang, H.-T.* and Chen, L.-P. (2023). R package "AFFECT" for Accelerated functional failure time model with error-contaminated survival times. Link: https://cran.r-project.org/web/packages/AFFECT/index.html

4. Hsu, W.-H.* and Chen, L.-P. (2023). R package "CHEMIST'' for Causal inference with high-dimensional error-prone covariates and misclassified treatments. Link: https://cran.r-project.org/web/packages/CHEMIST/index.html

5. Lin, C.-K.*, Chen, L.-P., and Yang, S.-F. (2022). R package "EATME" for EWMA-p control charts with correction of measurement error. Link: https://cran.r-project.org/web/packages/EATME/index.html

6. OuYang, Q.-Y.* and Chen, L.-P. (2022). Python package "BOOME" for Boosting algorithm for measurement error in binary responses and high-dimensional covariates. Link: https://pypi.org/project/BOOME/0.0.2/

7. Qiu, B.* and Chen, L.-P. (2022). R package "SIMEXBoost" for Boosting  method  for high-dimensional error-prone data. Link: https://cran.r-project.org/web/packages/SIMEXBoost/index.html