Imports: dplyr, httr, mapSpain (>= 0.6.0), rappdirs (>= 0.3.0), sf (>=
Suggests: ggplot2, knitr, png, rmarkdown, slippymath, testthat (>=

Title: Categorical Variable Selection Methods
Description: A multi-factor dimensionality reduction based forward selection method for genetic association mapping.

Title: Powerful Test for Survival Data under Non-Proportional Hazards
Description: An omnibus test of change-point Cox regression models to improve the statistical power of detecting signals of non-proportional hazards patterns. The technical details can be found in Hong Zhang, Qing Li, Devan Mehrotra and Judong Shen (2021) <arXiv:2101.00059>. Extensive simulation studies demonstrate that, compared to existing tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small alpha levels; 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient.

Title: Calculate Correlations and Estimate Causality
person("Tomomi", "Sugiyama", email = "sugiyama.t.am@m.titech.ac.jp", role = c("aut", "cre"))
Description: This tool performs pairwise correlation analysis and estimate causality.
Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.
Imports: cowplot, dplyr, ggplot2, grDevices, magrittr, stats, WriteXLS
Packaged: 2022-05-12 02:22:05 UTC; sugiyamatomomi

Packaged: 2022-05-24 05:53:57 UTC; sugiyamatomomi

Title: Methods for Estimating Causal Effects from Observational Data
Authors@R: c(person("Ricardo", "Silva", email = "ricardo@stats.ucl.ac.uk", role = c("cre", "aut")),
person("Robin", "Evans", email = "robin.evans@stats.ox.ac.uk", role = "aut"))
Description: Estimate causal effects of one variable on another, currently for
binary data only. Methods include instrumental variable bounds, adjustment by a
given covariate set, adjustment by an induced covariate set using a variation of
the PC algorithm, and an effect bounding method (the Witness Protection Program)
based on covariate adjustment with observable independence constraints.