jackstraw: Statistical Inference for Unsupervised Learning

Test for association between the observed data and their systematic patterns of variations. The jackstraw enables statistical testing for association between observed variables and latent variables, as captured by principal component analysis (PCA), factor analysis (FA), or other estimates. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds subpopulations among the observed variables. The jackstraw estimates statistical significance of cluster membership, including unsupervised evaluation of cell identities in single cell RNA-seq. P-values and posterior probabilities allows one to rigorously evaluate the strength of cluster membership assignments. See the GitHub repository for the latest developments and further helps.

Version: 1.3.1
Depends: R (≥ 3.0.0)
Imports: corpcor, cluster, methods, ClusterR, qvalue, lfa, stats, irlba, rsvd
Suggests: parallel, knitr, rmarkdown
Published: 2021-06-11
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
Package source: jackstraw_1.3.1.tar.gz
Windows binaries: r-devel: jackstraw_1.3.1.zip, r-release: jackstraw_1.3.1.zip, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-release (x86_64): not available, r-oldrel: not available
Old sources: jackstraw archive

Reverse dependencies:

Reverse imports: edge


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