Package: PICBayes 1.0

Chun Pan

PICBayes: Bayesian Models for Partly Interval-Censored Data

Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Authors:Chun Pan

PICBayes_1.0.tar.gz
PICBayes_1.0.tar.gz(r-4.7-any)PICBayes_1.0.tar.gz(r-4.6-any)
PICBayes_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PICBayes/json (API)

# Install 'PICBayes' in R:
install.packages('PICBayes', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • C - Adjacency matrix of 46 South Carolina counties
  • da1 - Partly interva-censored data
  • da2 - Clustered partly interva-censored data
  • da3 - Clustered partly interva-censored data
  • da4 - Clustered partly interva-censored data
  • mCRC - Colorectal cancer data

On CRAN:

Conda:

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

1.00 score 4 scripts 273 downloads 24 exports 10 dependencies

Last updated from:79becb9f75. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK188
source / vignettesOK178
linux-release-x86_64OK183
wasm-releaseOK101

Exports:clusterIC_intclusterIC_int_DPclusterIC_trtclusterIC_trt_DPclusterIC_ZclusterIC_Z_DPclusterPIC_intclusterPIC_int_DPclusterPIC_trtclusterPIC_trt_DPclusterPIC_ZclusterPIC_Z_DPcoef.PICBayesIClogLik.PICBayesPICPICBayesPICBayes.defaultPICBayes.formulaplot.PICBayesspatialICspatialPICsummary.PICBayesSurvtoLR

Dependencies:codalatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregSparseMsurvival

Readme and manuals

Help Manual

Help pageTopics
Bayesian Models for Partly Interval-Censored Data and General Interval-Censored DataPICBayes-package
Adjacency matrix of 46 South Carolina countiesC
PH model with random intercept for clustered general interval-censored dataclusterIC_int
PH model with random intercept for clustered general interval-censored dataclusterIC_int_DP
PH model with random intercept and random treatment for clustered general interval-censored dataclusterIC_trt
PH model with random intercept and random treatment for clustered general interval-censored dataclusterIC_trt_DP
Mixed effects PH model for clustered general interval-censored dataclusterIC_Z
Mixed effects PH model for clustered general interval-censored dataclusterIC_Z_DP
PH model with random intercept for clustered partly interval-censored dataclusterPIC_int
PH model with random intercept for clustered partly interval-censored data dataclusterPIC_int_DP
PH model with random intercept and random treatment for clustered partly interval-censored dataclusterPIC_trt
PH model with random intercept and random treatment for clustered partly interval-censored dataclusterPIC_trt_DP
Mixed effects PH model for clustered partly interval-censored dataclusterPIC_Z
Mixed effects PH model for clustered partly interval-censored dataclusterPIC_Z_DP
Coef method for a PICBayes modelcoef.PICBayes
Partly interva-censored datada1
Clustered partly interva-censored datada2
Clustered partly interva-censored datada3
Clustered partly interva-censored datada4
PH model for general interval-censored dataIC
LogLik method for a PICBayes modellogLik.PICBayes
Colorectal cancer datamCRC
PH model for partly interval-censored dataPIC
Bayesian models for partly interval-censored data and general interval-censored dataPICBayes PICBayes.default PICBayes.formula
Plot method for a PICBayes modelplot.PICBayes
PH model for spatial general interval-censored dataspatialIC
PH model for spatial partly interval-censored dataspatialPIC
Summary method for a PICBayes modelsummary.PICBayes
Transform Surv object to data matrix with L and R columnsSurvtoLR