Statistical Inference for Unsupervised Learning
Overview
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
- Commented out unimportant tests that fail on CRAN
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-04
- ERROR12 OK, 1 ERROR2026-09-02
- OK13 OK2026-08-05
- ERROR11 OK, 2 ERROR2026-07-11
- OK10 OK2026-04-25
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- NOTE12 OK, 2 NOTE2026-03-29
- ERROR11 OK, 2 NOTE, 1 ERROR2026-03-22
- NOTE12 OK, 2 NOTE2026-03-15
- ERROR11 OK, 2 NOTE, 1 ERROR2026-03-10
Documentation
- Help pages
- 20 (2 internal)includes a package overview page
- Examples that run
- 42%5 run in a plain check · 7 never run
- Exported functions with examples
- 75%
- Documented parameters
- 99%
- Return-value docs
- 94%
- Help pages citing references
- 75%
Downloads
Dependencies
Code & Tests
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- RR 4.6.0 released · 2026-04-24
- unarchivedReturned to CRAN2026-04-22
- archivedRemoved from CRAN2026-04-09issues were not corrected in time
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- unarchivedReturned to CRAN2024-02-07
- archivedRemoved from CRAN2024-01-11issues were not corrected in time
- RR 4.3.0 released · 2023-04-21
- unarchivedReturned to CRAN2022-11-25
- archivedRemoved from CRAN2022-05-23check issues were not corrected in time
- RR 4.2.0 released · 2022-04-22
- unarchivedReturned to CRAN2021-06-11
- archivedRemoved from CRAN2021-05-27check problems were not corrected in time
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
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- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
- RR 3.0.0 released · 2013-04-03
- 2.15.3Latest
- 1.3.21
- 1.3.172024-09-16 · diff ↗
- 1.3.15
- 1.3.92024-02-07 · diff ↗
- 1.3.8
- 1.3.12021-06-11 · diff ↗
- 1.3
- 1.22018-08-07 · diff ↗ ·
removed 5 exports
jackstraw, jackstraw.FUN, jackstraw.LFA, jackstraw.PCA, jackstraw.parametricadded 10 exports
find_k, jackstraw_MiniBatchKmeans, jackstraw_cluster, jackstraw_kmeans, jackstraw_kmeanspp, jackstraw_lfa, jackstraw_pam, jackstraw_pca, jackstraw_subspace, pip - 1.1.12017-09-04 · diff ↗
- 1.12015-12-29 · diff ↗ ·
added 5 exports
jackstraw.FUN, jackstraw.LFA, jackstraw.PCA, jackstraw.parametric, lfa.corpcor - 1.02014-01-31
- RR 2.15.0 released · 2012-03-30
Package metadata
- First published
- 2013-01-28
- Total releases
- 12 / 13 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.0.0
- Bundled data
- 264 KB / 1 file
- Download size
- 314 KB
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