Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.

We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>,  Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972>). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication.

Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.

Tools for keeping track of information, named
    "keys", about rows of data frame like objects. This is done by
    creating special attribute "keys" which is updated after every change
    in rows (subsetting, ordering, etc.).  This package is designed to
    work tightly with 'dplyr' package.

Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) <arXiv:2308.13383>.