Title: GEO RNA-Seq Experiments Processing Pipeline
Authors@R: c(person("Naim Al", "Mahi", role = c("cre", "aut"),
person("Mario", "Medvedovic", role = c("aut", "ctb")))
Maintainer: Naim Al Mahi <mahina@mail.uc.edu>
Description: An R based pipeline to download and process Gene Expression Omnibus (GEO) RNA-seq data.
For a given GEO series accession ID, this pipeline generates metadata,
both gene and transcript level counts, and quality control (QC) report.
Imports: XML, rentrez, RCurl, GEOquery, Biobase, parallel, tximport,
SystemRequirements: SRAtoolkit, Salmon, Java, FastQC, MultiQC

This package is mainly developed to process the GEO RNA-seq datasets
for the web platform GREIN. More details about GREP2 and GREIN can be

Title: Bivariate Copula Functions Based on Regular Grid
Description: Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.
Authors@R: c(person("Rogelio Salinas Gutiérrez", "Developer" , role = c("aut", "cre","cph"),
person("Angélica Hernández Quintero", "Dev", role = "aut"),
person("Pedro Abraham Montoya Calzada", "Dev", role = "cph"))
Imports: Rsolnp, fields, limSolve, ggplot2, pracma, reshape2
Author: Rogelio Salinas Gutiérrez Developer [aut, cre, cph],
Maintainer: Rogelio Salinas Gutiérrez Developer <rsalinas@correo.uaa.mx>

Title: General Regression Neural Networks Package
Description: This General Regression Neural Networks Package uses various distance functions.
It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper
Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions,
although "euclidean" distance is used traditionally.
Imports: cvTools, rdist, scales, stats, vegan
Packaged: 2021-09-06 09:43:08 UTC; paleowind
Author: Shufeng LI [aut, cre] (<https://orcid.org/0000-0002-5144-4432>)

Author: Nelson Nazzicari & Filippo Biscarini
Description: Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
Suggests: BGLR, e1071, ggplot2, knitr, randomForest