This package is considered a duplicate. The official version of this package is found at:https://weichisite.r-universe.dev/LTRCforests

Package: LTRCforests 0.7.0

Weichi Yao

LTRCforests: Ensemble Methods for Survival Data with Time-Varying Covariates

Implements the conditional inference forest and relative risk forest algorithm to modeling left-truncated right-censored data with time-invariant covariates, and (left-truncated) right-censored survival data with time-varying covariates. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2022) <doi:10.1177/09622802221111549>.

Authors:Weichi Yao [aut, cre], Halina Frydman [aut], Denis Larocque [aut], Jeffrey S. Simonoff [aut]

LTRCforests_0.7.0.tar.gz
LTRCforests_0.7.0.zip(r-4.5)LTRCforests_0.7.0.zip(r-4.4)LTRCforests_0.7.0.zip(r-4.3)
LTRCforests_0.7.0.tgz(r-4.4-x86_64)LTRCforests_0.7.0.tgz(r-4.4-arm64)LTRCforests_0.7.0.tgz(r-4.3-x86_64)LTRCforests_0.7.0.tgz(r-4.3-arm64)
LTRCforests_0.7.0.tar.gz(r-4.5-noble)LTRCforests_0.7.0.tar.gz(r-4.4-noble)
LTRCforests_0.7.0.tgz(r-4.4-emscripten)LTRCforests_0.7.0.tgz(r-4.3-emscripten)
LTRCforests.pdf |LTRCforests.html✨
LTRCforests/json (API)

# Install 'LTRCforests' in R:

install.packages('LTRCforests', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

Datasets:

pbcsample - Sample Mayo Clinic Primary Biliary Cirrhosis Data

On CRAN:

archived packages r-universe

1.81 score 5 stars 13 scripts 213 downloads 7 exports 31 dependencies

Last updated 11 days agofrom:6a7b7b2d2a (on package/LTRCforests). Checks:7 OK, 2 ERROR. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 10 2025
R-4.5-win-x86_64	ERROR	Jan 10 2025
R-4.5-linux-x86_64	ERROR	Jan 10 2025
R-4.4-win-x86_64	OK	Jan 10 2025
R-4.4-mac-x86_64	OK	Jan 10 2025
R-4.4-mac-aarch64	OK	Jan 10 2025
R-4.3-win-x86_64	OK	Jan 10 2025
R-4.3-mac-x86_64	OK	Jan 10 2025
R-4.3-mac-aarch64	OK	Jan 10 2025

Exports:ltrccif ltrcrrf predictProb print sbrier_ltrc tune.ltrccif tune.ltrcrrf

Dependencies:class cli codetools data.table diagram digest Formula future future.apply globals inum ipred KernSmooth lattice lava libcoin listenv MASS Matrix mvtnorm nnet numDeriv parallelly partykit prodlim progressr Rcpp rpart shape SQUAREM survival

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Constructs forest methods for left-truncated and right-censored (LTRC) survival data	LTRCforests-package
Fit a LTRC conditional inference forest	ltrccif
Fit a LTRC relative risk forest	ltrcrrf
Sample Mayo Clinic Primary Biliary Cirrhosis Data	pbcsample
Compute a Survival Curve from a LTRCCIF model or a LTRCRRF model	predictProb predictProb.ltrccif, predictProb.ltrcrrf
Print Summary Output of a ltrccif object or a ltrcrrf object	print print.ltrccif, print.ltrcrrf
Model fit evaluation for LTRC forests.	sbrier_ltrc
Tune 'mtry' to the optimal value with respect to out-of-bag error for a LTRCCIF model	tune.ltrccif
Tune 'mtry' to the optimal value with respect to out-of-bag error for a LTRCRRF model	tune.ltrcrrf