Depends: R (>= 2.10.0), stats, graphics, utils, tcltk, psych

Description: Visually and statistically analyze single system data

Depends: R (>= 2.10.0) ,psych,TTR,MASS,tcltk

person(given = "Wendy", family = "Zeitlin", role = "aut", email = "zeitlinw@montclair.edu"))

Title: Searching Shared HLA Amino Acid Residue Prevalence
Description: Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, and calculates the frequencies of those motifs based on HLA allele frequency data. 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) uses 'Generic Mapping Tools (GMT)' software and the 'GMT' R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. 'SSHAARP' analyzes the allele frequency data described by Solberg et al. (2008) <doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total.
Imports: data.table, stringr, gtools, BIGDAWG, gmt, DescTools, dplyr,
SystemRequirements: GMT (5 or 6), Ghostscript (>=9.6)
Packaged: 2020-04-22 01:40:20 UTC; liviatran

Description: Semi-supervised learning has attracted the attention of machine learning community because of its high accuracy with less annotating effort compared with supervised learning.The question that semi-supervised learning wants to address is: given a relatively small labeled dataset and a large unlabeled dataset, how to design classification algorithms learning from both ? This package is a collection of some classical semi-supervised learning algorithms in the last few decades.
Packaged: 2016-05-14 13:26:18 UTC; Wangjunxiang

Description: Efficient algorithms for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO.

Description: Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO. Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.