Title: Phylogenetic Analysis of RADseq Data
Authors@R: c(person("Andrew", "Hipp", email="ahipp@mortonarb.org", role = c("aut", "cre")),
person("Klaus", "Schliep", role = c("ctb"))
Description: Implements import, export, manipulation, visualization, and downstream
(post-clustering) analysis of RADseq data, integrating with the 'pyRAD' package by Deren Eaton.
Imports: geiger, Biostrings, phangorn, IRanges, hierfstat

Title: Normalization and Study of Rank Abundance Distributions
Description: It has tools for normalization of rank abundance
distributions (RAD) to a desired number of ranks using MaxRank
RADs are commonly used in biology/ecology and mathematically equivalent
to complementary cumulative distributions (CCDFs) which are used in
physics, linguistics and sociology and more generally in data science.
Imports: sfsmisc, scales, stats, graphics
person("Mohmmadkarim", "Saeedghalati", email = "m.saeedghalati@uni-due.de", role = c("aut", "cre")),
person("Farnoush", "Farahpour", email = "farnoush.farahpour@uni-due.de", role = c("aut")),
Author: Mohmmadkarim Saeedghalati [aut, cre],

Title: Optimize the De Novo Stacks Pipeline via R
Description: Offers a handful of useful wrapper functions which streamline
the reading, analyzing, and visualizing of variant call format (vcf) files in R.
This package was designed to facilitate an explicit pipeline for optimizing
parameters during de novo (without a reference genome) assembly and variant
calling of restriction-enzyme associated DNA sequence (RADseq) data. The
pipeline implemented here is based on the 2017 paper "Lost in Parameter Space"
establishes clear recommendations for optimizing the parameters
'm', 'M', and 'n', during the process of assembling loci.
Imports: vcfR, ggplot2, ggridges, gridExtra
Author: Devon DeRaad [aut, cre] (<https://orcid.org/0000-0003-3105-985X>)

Title: Random Approximate Elastic Net (RAEN) Variable Selection Method
Description: The Proportional Subdistribution Hazard (PSH) model has been popular for estimating the effects of the covariates on the cause of interest in Competing Risks analysis. The fast accumulation of large scale datasets has posed a challenge to classical statistical methods. Current penalized variable selection methods show unsatisfactory performance in ultra-high dimensional data. We propose a novel method, the Random Approximate Elastic Net (RAEN), with a robust and generalized solution to the variable selection problem for the PSH model. Our method shows improved sensitivity for variable selection compared with current methods.
Imports: boot, foreach, doParallel,glmnet, fastcmprsk