R packages by jrjthompson

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:10.48550/arXiv.2306.01890>> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.

Last updated 5 months ago

3.00 score 3 scripts 181 downloads

nonsmooth - Nonparametric Methods for Smoothing Nonsmooth Data

Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.

Last updated 8 months ago

change-pointchange-point-modelingchangepointkernelkernel-methodsnonparametric-regressionsmoothingsmoothing-methods

2.48 score 217 downloads