Package: kdml 1.1.1

kdml: Kernel Distance Metric Learning for Mixed-Type Data

Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) <doi:10.1007/s00357-024-09493-z> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.

Authors:John R. J. Thompson [aut, cre], Jesse S. Ghashti [aut]

kdml_1.1.1.tar.gz
kdml_1.1.1.zip(r-4.5)kdml_1.1.1.zip(r-4.4)kdml_1.1.1.zip(r-4.3)
kdml_1.1.1.tgz(r-4.5-any)kdml_1.1.1.tgz(r-4.4-any)kdml_1.1.1.tgz(r-4.3-any)
kdml_1.1.1.tar.gz(r-4.5-noble)kdml_1.1.1.tar.gz(r-4.4-noble)
kdml_1.1.1.tgz(r-4.4-emscripten)kdml_1.1.1.tgz(r-4.3-emscripten)
kdml.pdf |kdml.html
kdml/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.18 score 4 scripts 267 downloads 7 exports 15 dependencies

Last updated 1 months agofrom:f6e0a566ce. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-winOKMar 23 2025
R-4.5-macOKMar 23 2025
R-4.5-linuxOKMar 23 2025
R-4.4-winOKMar 23 2025
R-4.4-macOKMar 23 2025
R-4.4-linuxOKMar 23 2025
R-4.3-winOKMar 23 2025
R-4.3-macOKMar 23 2025

Exports:confactorddkpsdksskssmscv.dkpsmscv.dkssspectral.clust

Dependencies:bootcommonmarkcubaturelatticemarkdownMASSMatrixMatrixModelsnpquadprogquantregRcppSparseMsurvivalxfun

kdml package

Rendered fromkdml.Rmdusingknitr::rmarkdownon Mar 23 2025.

Last update: 2024-08-28
Started: 2024-08-28