PARSE: Model-Based Clustering with Regularization Methods for High-Dimensional Data
Version 0.1.0

Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.

Browse man pages Browse package API and functions Browse package files

AuthorLulu Wang, Wen Zhou, Jennifer Hoeting
Date of publication2016-06-11 09:42:05
MaintainerLulu Wang <wanglulu@stat.colostate.edu>
LicenseCC0
Version0.1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("PARSE")

Man pages

apfp: Model-based Clustering with APFP
apL1: Model-based Clustering with APL1
heatmap_fit: summary plot of globally and pairwise informative variables
nopenalty: Classical Model-based Clustering
parse: Model-based Clustering with PARSE
response2drug: Gene-expression Data for Asthma Disease
summary: summary of the clustering results

Functions

apL1 Man page Source code
apfp Man page Source code
apfp_LQA Source code
count.mu Source code
dmvnorm_log Source code
heatmap_fit Man page Source code
nopenalty Man page Source code
obj_parse_fn Source code
parse Man page Source code
parse_backward Source code
response2drug Man page
summary Man page Source code
summary.parse_fit Man page Source code

Files

NAMESPACE
data
data/response2drug.rda
R
R/apfp.R
R/parse_backward.R
R/summary_parse_fit.R
R/data_response2drug.R
R/nopenalty.R
R/PARSE_package.R
R/additional_fn.R
R/parse.R
R/apL1.R
R/plot_parse_fit.R
R/apfp_optim.R
MD5
DESCRIPTION
man
man/parse.Rd
man/response2drug.Rd
man/heatmap_fit.Rd
man/apfp.Rd
man/apL1.Rd
man/summary.Rd
man/nopenalty.Rd
PARSE documentation built on May 19, 2017, 11:30 p.m.