Author: Timothy A. Myers [aut, cre] (<https://orcid.org/0000-0001-8127-3446>),
Mitchell J. Machiela [aut] (<https://orcid.org/0000-0001-6538-9705>)

Title: Exact tests for Linkage Disequilibrium and Hardy-Weinberg Equilibrium
Description: Exact tests for Linkage Disequilibrium (LD) and Hardy-Weinberg Equilibrium (HWE). - 2-sided LD tests based on different measures of LD (Kulinskaya and Lewin 2008) - 1-sided Fisher's exact test for LD - 2-sided Haldane test for HWE (Wiggington 2005) - 1-sided test for inbreeding - conditional p-values proposed in Kulinskaya (2008) to overcome the problems of asymetric distributions (for both LD and HWE)
Packaged: Mon Jun  9 10:25:16 2008; alewin

Title: Finds "Local Subnetworks" Within an Interaction Network which
Show Enrichment for Differentially Expressed Genes
Description: Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
Suggests: knitr, doMC, rmarkdown, ROCR, testthat

Title: Constructing Gene Co-Expression Networks for Single-Cell
RNA-Sequencing Data Using Pseudotime Ordering
Maintainer: Alicia T. Specht <aspecht2@nd.edu>
Description: Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.