Package: icenReg 2.0.16

icenReg: Regression Models for Interval Censored Data

Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data.

Authors:Clifford Anderson-Bergman

icenReg_2.0.16.tar.gz
icenReg_2.0.16.zip(r-4.7)icenReg_2.0.16.zip(r-4.6)icenReg_2.0.16.zip(r-4.5)
icenReg_2.0.16.tgz(r-4.6-x86_64)icenReg_2.0.16.tgz(r-4.6-arm64)icenReg_2.0.16.tgz(r-4.5-x86_64)icenReg_2.0.16.tgz(r-4.5-arm64)
icenReg_2.0.16.tar.gz(r-4.7-arm64)icenReg_2.0.16.tar.gz(r-4.7-x86_64)icenReg_2.0.16.tar.gz(r-4.6-arm64)icenReg_2.0.16.tar.gz(r-4.6-x86_64)
icenReg_2.0.16.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
icenReg/json (API)

# Install 'icenReg' in R:

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

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

IR_diabetes - Interval censored time from diabetes onset to diabetic nephronpathy
miceData - Lung Tumor Interval Censored Data from Hoel and Walburg 1972

On CRAN:

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

cpp

6.94 score 2 stars 20 packages 190 scripts 6.3k downloads 6 mentions 22 exports 10 dependencies

Last updated from:26fadac37c. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	209
linux-devel-x86_64	OK	216
source / vignettes	OK	287
linux-release-arm64	OK	174
linux-release-x86_64	OK	155
macos-release-arm64	OK	151
macos-release-x86_64	OK	285
macos-oldrel-arm64	OK	153
macos-oldrel-x86_64	OK	291
windows-devel	OK	160
windows-release	OK	157
windows-oldrel	OK	146
wasm-release	OK	157

Exports:bayesControls cs2ic diag_baseline diag_covar getFitEsts getSCurves ic_bayes ic_np ic_par ic_sample ic_sp imputeCens ir_clustBoot lines.icenReg_fit makeCtrls_icsp plot.icenReg_fit sampleSurv simCS_weib simDC_weib simIC_cluster simIC_weib survCIs

Dependencies:coda codetools foreach iterators lattice Matrix MLEcens Rcpp RcppEigen survival

Using icenReg

Rendered fromicenReg.Rnwusingutils::Sweave

Last update: 2020-10-04
Started: 2015-12-10

Citation

Readme and manuals

Help Manual

Help page	Topics
Control parameters for ic_bayes	bayesControls
Convert current status data into interval censored format	cs2ic
Compare parametric baseline distributions with semi-parametric baseline	diag_baseline
Evaluate covariate effect for regression model	diag_covar
Get Survival Curve Estimates from icenReg Model	getFitEsts
Get Estimated Survival Curves from Semi-parametric Model for Interval Censored Data	getSCurves
Bayesian Regression Models for Interval Censored Data	ic_bayes
Non-Parametric Estimator for Interval Censored Data	ic_np
Parametric Regression Models for Interval Censored Data	ic_par
Draw samples from an icenReg model	ic_sample
Semi-Parametric models for Interval Censored Data	ic_sp
Impute Interval Censored Data from icenReg Regression Model	imputeCens
Updates the covariance using cluster bootstrap	ir_clustBoot
Interval censored time from diabetes onset to diabetic nephronpathy	IR_diabetes
Plotting for icenReg Fits	lines.icenReg_fit
Control Parameters for ic_sp	makeCtrls_icsp
Lung Tumor Interval Censored Data from Hoel and Walburg 1972	miceData
Plotting for icenReg Fits	plot.icenReg_fit
Predictions from icenReg Regression Model	predict.icenReg_fit
Samples fitted survival function	sampleSurv
Simulate Current Status Data	simCS_weib
Simulate Doubly Censored Data	simDC_weib
Simulates data with multiple observations per subject	simIC_cluster
Simulates interval censored data from regression model with a Weibull baseline	simIC_weib
Confidence/Credible intervals for survival curves	survCIs

Package: icenReg 2.0.16

icenReg: Regression Models for Interval Censored Data

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)