Smoother Change Tracking and Versioning for R Packages
c(person(given = "Kirill",
family = "M\u00fcller",
role = c("aut", "cre", "cph"),
email = "krlmlr+r@mailbox.org",
comment = c(ORCID = "0000-0002-1416-3412")),
person(given = "Patrick",
family = "Schratz",
role = "aut",
email = "patrick.schratz@gmail.com",
comment = c(ORCID = "0000-0003-0748-6624")),
person(given = "Maëlle",
family = "Salmon",
role = "ctb",
email = "msmaellesalmon@gmail.com",
comment = c(ORCID = "0000-0002-2815-0399")))
Streamlines the process of updating changelogs (NEWS.md)
and versioning R packages developed in git repositories.
mockery, covr, fansi, fs, knitr, parsedate, rmarkdown,
rstudioapi, testthat (>= 3.0.0), brio, callr, pkgload

Incarbone G., Mazza A., Punzo A., Ingrassia S.

This package implements a flexible version of the Cluster Weighted Model (CWM), which is an eminent member of the class of mixtures of regression models with random covariates.

Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Flexible crossing hazards in the Cox model
Vito M.R. Muggeo <vito.muggeo@unipa.it> and Miriam Tagliavia
<tagliavia@dssm.unipa.it>
Crossing points in time varying hazard ratios.

Flexible Parametric Models for Censored and Truncated Data
Estimation of flexible parametric models for survival data.

Friedrich Leisch, parts based on code by Evgenia Dimitriadou
The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary
distance/similarity measures and centroid computation. Further
cluster methods include hard competitive learning, neural gas and QT
clustering.

graphics, lattice, modeltools (>= 0.2-15)

graphics, grid, lattice, modeltools (>= 0.2-16)
The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary
distance measures and centroid computation. Further
cluster methods include hard competitive learning, neural gas, and QT
clustering.