Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.

Computes mutual information matrices from continuous, categorical 
  and survival variables, as well as feature selection with minimum redundancy, 
  maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
  De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.

Computes multiple correlation coefficient when the data matrix is given and tests its significance.

A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC 
            (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also 
            contains functions to simulate data under a certain topology, to visualize a graph in different 
            ways, and to compare graphs and quantify the differences. 
            See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,
            Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>,
            Kvamme and Badsha, et al. (2025) <doi:10.1093/genetics/iyaf064>.

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.