principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single
matrixStats (>= 0.58.0), stats, rlang (>= 0.4.10), purrr (>=
ggplot2 (>= 3.3.3), readxl (>= 1.3.1), Amelia, rmarkdown

ggplot2 (>= 3.3.3), readxl (>= 1.3.1), Amelia, rmarkdown, utils

Description: A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment"
hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons.
It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.
Imports: openxlsx (>= 4.2.3), stats, rlang (>= 0.4.10), ggplot2 (>=
Suggests: rmarkdown, spelling, knitr, testthat (>= 3.0.0), Compind,

Title: Causal Discovery for Categorical Data with Label Permutation
Authors@R: person(given = "Yang", family = "Ni", email = "yni@stat.tamu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0636-2363"))
Description: Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".

Title: A Combined Association Test for Genes using Summary Statistics
Author: Minghui Wang, Jianfei Huang, James B. Potash, Shizhong Han
Description: To compute gene-based genetic association statistics from P values at multiple SNPs and genotype data of ancestry matched reference samples. COMBined Association Test (COMBAT) incorporates strengths from multiple existing gene-based tests, including VEGAS, GATES and SimpleM, and achieves much improved performance than any individual test.

Author: Minghui Wang, Yiyuan Liu, Shizhong Han
Description: Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal.  Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.