Package: nipals 0.9

nipals: Principal Components Analysis using NIPALS or Weighted EMPCA, with Gram-Schmidt Orthogonalization

Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares or weighted Expectation Maximization PCA with Gram-Schmidt orthogonalization of the scores and loadings. Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.

Authors:Kevin Wright [aut, cre, cph]

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nipals.pdf |nipals.html
nipals/json (API)
NEWS

# Install 'nipals' in R:
install.packages('nipals', repos = c('https://kwstat.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/kwstat/nipals/issues

Datasets:
  • uscrime - U.S. Crime rates per 100,00 people

On CRAN:

3 exports 7 stars 2.12 score 0 dependencies 4 dependents 32 scripts 577 downloads

Last updated 2 months agofrom:765bef02a7. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 14 2024
R-4.5-winNOTESep 14 2024
R-4.5-linuxNOTESep 14 2024
R-4.4-winNOTESep 14 2024
R-4.4-macNOTESep 14 2024
R-4.3-winNOTESep 14 2024
R-4.3-macNOTESep 14 2024

Exports:avg_angular_distanceempcanipals

Dependencies:

Comparing results and performance of NIPALS functions in R

Rendered fromnipals_comparisons.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2020-01-20
Started: 2017-10-30

EMPCA notes

Rendered fromempca_notes.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2024-01-26
Started: 2020-01-20

The NIPALS algorithm

Rendered fromnipals_algorithm.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2020-01-20
Started: 2017-10-31

NIPALS optimization notes

Rendered fromnipals_optimization.Rmdusingknitr::rmarkdownon Sep 14 2024.

Last update: 2024-01-26
Started: 2017-10-31