jackstraw: Statistical Inference for Unsupervised Learning

Test for association between the observed data and their systematic patterns of variations, that are often extracted by unsupervised learning. Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. This allows one to, for example, obtain principal components (PCs) and conduct rigorous statistical testing for association between observed variables and PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and other algorithms, finds subpopulations among the observed variables. The jackstraw test can estimate statistical significance of cluster membership, so that one can evaluate the strength of membership assignments. This package also includes several related methods to support statistical inference and probabilistic feature selection for unsupervised learning.

Version: 1.2
Depends: R (≥ 3.0.0)
Imports: corpcor, cluster, ClusterR, qvalue, methods, lfa, stats
Suggests: parallel, knitr, rmarkdown
Published: 2018-08-07
Author: Neo Christopher Chung, John D. Storey, Wei Hao
Maintainer: Neo Christopher Chung <nchchung at gmail.com>
BugReports: https://github.com/ncchung/jackstraw/issues
License: GPL-2
URL: https://github.com/ncchung/jackstraw
NeedsCompilation: no
Materials: README
CRAN checks: jackstraw results


Reference manual: jackstraw.pdf
Vignettes: jackstraw: Statistical Inference using Latent Variables
Package source: jackstraw_1.2.tar.gz
Windows binaries: r-devel: jackstraw_1.2.zip, r-release: jackstraw_1.2.zip, r-oldrel: jackstraw_1.2.zip
OS X binaries: r-release: jackstraw_1.2.tgz, r-oldrel: jackstraw_1.2.tgz
Old sources: jackstraw archive


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