Title: Standardized Comparison of Workflows in Mass Spectrometry-Based
Description: Useful functions to analyze proteomic workflows including
number of identifications, data completeness, missed cleavages,
quantitative and retention time precision etc. Various software
outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut', 'DIA-NN'
Imports: comprehenr, data.table, dplyr, flowTraceR, forcats, ggplot2,
magrittr, plotly, purrr, stats, stringr, tibble, tidyr, UpSetR
Suggests: flextable, knitr, rmarkdown, testthat (>= 3.0.0), utils

Title: Marginal regresson models for dependent data
Depends: R (>= 2.0.0), nlme, sandwich, sn, geoR
Description: likelihood inference in a class of marginal regression
models for non-normal responses with a flexible specification

Title: Penalized Inverse-Variance Weighted Estimator for Mendelian
Description: 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>.

Title: Two Sample Mendelian Randomization using Robust Adjusted Profile
Authors@R: c(person("Qingyuan", "Zhao", email = "qingyzhao@gmail.com", role = c("aut", "cre")))
Description: Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.

Title: Multilocus Random Mixed Linear Model for Genome-Wide Association
Author: Wenlong Ren,Shibo Wang and Yuanming Zhang
Description: Conduct multi-locus random-SNP-effect mixed linear model analysis for genome-wide association study. First, each marker is scanned using random-SNP-effect mixed linear model method. Then, all the markers selected from the first step are included in the multi-locus model and their effects are estimated by an EM empirical Bayes method. No multiple test correction is needed.
Packaged: 2016-02-26 07:31:46 UTC; Administrator