ksm: Kernel Density Estimation for Random Symmetric Positive Definite Matrices

Kernel smoothing for Wishart random matrices described in Daayeb, Khardani and Ouimet (2025) <doi:10.48550/arXiv.2506.08816>, Gaussian and log-Gaussian models using least square or likelihood cross validation criteria for optimal bandwidth selection.

Version: 1.1
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 1.0.12)
LinkingTo: Rcpp, RcppArmadillo
Suggests: cubature, rmarkdown, knitr, tinytest
Published: 2026-06-07
DOI: 10.32614/CRAN.package.ksm
Author: Leo Belzile ORCID iD [aut, cre], Frederic Ouimet ORCID iD [aut]
Maintainer: Leo Belzile <belzilel at gmail.com>
BugReports: https://github.com/lbelzile/ksm/issues
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: ksm results

Documentation:

Reference manual: ksm.html , ksm.pdf
Vignettes: Overview of the ksmpackage (source, R code)

Downloads:

Package source: ksm_1.1.tar.gz
Windows binaries: r-devel: ksm_1.0.zip, r-release: ksm_1.0.zip, r-oldrel: ksm_1.0.zip
macOS binaries: r-release (arm64): ksm_1.1.tgz, r-oldrel (arm64): ksm_1.1.tgz, r-release (x86_64): ksm_1.1.tgz, r-oldrel (x86_64): ksm_1.1.tgz
Old sources: ksm archive

Linking:

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