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RMLPCA

Maximum Likelihood Principal Component Analysis

v0.0.1 · Nov 5, 2020 · MIT + file LICENSE

Description

R implementation of Maximum Likelihood Principal Component Analysis The main idea of this package is to have an alternative way of PCA for subspace modeling that considers measurement errors. More details can be found in Peter D. Wentzell (2009) <doi:10.1016/B978-0-444-64165-6.03029-9>.

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r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-windows-x86_64 OK
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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies Matrix pracma RSpectra RMLPCA

Version History

1 tracked
new 0.0.1 Mar 10, 2026

R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Nov 5, 2020. Releases before tracking aren’t shown.