dplyr, httr, mapSpain (>= 0.6.0), rappdirs (>= 0.3.0), sf (>=
1.0.0), stringi, terra, tibble, xml2
ggplot2, knitr, png, rmarkdown, slippymath, testthat (>=
3.0.0), tidyterra

A multi-factor dimensionality reduction based forward selection method for genetic association mapping.

Powerful Test for Survival Data under Non-Proportional Hazards
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.

Calculate Correlations and Estimate Causality
person("Tomomi", "Sugiyama", email = "sugiyama.t.am@m.titech.ac.jp", role = c("aut", "cre"))
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.
cowplot, dplyr, ggplot2, grDevices, magrittr, stats, WriteXLS

Methods for Estimating Causal Effects from Observational Data
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"))
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.
Ricardo Silva [cre, aut],
Robin Evans [aut]

Estimation of Causal Effects with Generalized Additive Models
Adam Glynn <aglynn@iq.harvard.edu>, Kevin Quinn
<kquinn@law.berkeley.edu>
This package implements various estimators for average
treatment effects---an inverse probability weighted (IPW)
estimator, an augmented inverse probability weighted (AIPW)
estimator, and a standard regression estimator---that make use
of generalized additive models for the treatment assignment
model and/or outcome model.