c(
person(
"Doug","Kelkhoff",
email = "doug.kelkhoff@gmail.com",
role = c("aut", "cre")
),
person(
"Bill", "Denney",
email = "wdenney@humanpredictions.com",
role = "ctb",
comment = c(ORCID = "0000-0002-5759-428X")
)
)
Datetimes and timestamps are invariably an imprecise notation, with any
partial representation implying some amount of uncertainty. To handle this,
'parttime' provides classes for embedding partial missingness as a central
part of its datetime classes. This central feature allows for more ergonomic
use of datetimes for challenging datetime computation, including
calculations of overlapping date ranges, imputations, and more thoughtful
handling of ambiguity that arises from uncertain time zones. This package was
developed first and foremost with pharmaceutical applications in mind, but
aims to be agnostic to application to accommodate general use cases just as
conveniently.
crayon, lubridate, methods, pillar, utils, vctrs (>= 0.2.0)
dplyr, withr, testthat, knitr, rmarkdown
Doug Kelkhoff [aut, cre],
Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>)

A Laboratory for Recursive Part(y)itioning
Torsten Hothorn, Kurt Hornik and Achim Zeileis
Unbiased recursive partitioning in a conditional
inference framework. This package implements a unified
framework for recursive partitioning which embeds tree-structured
regression models into a well defined theory of conditional inference
procedures. Stopping criteria based on multiple test procedures
are implemented. The methodology is applicable to all kinds of
regression problems, including nominal, ordinal, numeric, censored
as well as multivariate response variables and arbitrary measurement
scales of the covariates. Extensible functionality for visualizing
tree-structured regression models is available.
R (>= 2.0.1), survival, grid, modeltools, coin

A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates. Similar
in spirit, the function mob() implements an algorithm for recursive
partitioning based on parametric models (e.g. linear models, GLMs or
survival regression) based on parameter instability tests. Furthermore,
cforest() provides an implementation of Breiman's random forests
based on conditional inference trees. Extensible functionality for
visualizing tree-structured regression models is available.
R (>= 2.0.1), survival, grid, modeltools (>= 0.2-3), coin (>= 0.3-8),
zoo, sandwich (>= 1.1-1), strucchange