Package: BASSLINE 0.0.0.9010

BASSLINE: Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions

Mixtures of life distributions provide a convenient framework for survival analysis: particularly when standard models such as the Weibull are unable to capture some features from the data. These mixtures can also account for unobserved heterogeneity or outlying observations. BASSLINE uses shape mixtures of log-normal distributions and has particular applicability to data with fat tails.

Authors:Catalina Vallejos [aut], Nathan Constantine-Cooke [cre, aut]

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manual.pdf |manual.html✨
card.svg |card.png
BASSLINE/json (API)
NEWS

# Install 'BASSLINE' in R:
install.packages('BASSLINE', repos = c('https://nathansam.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nathansam/bassline/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • cancer - VA Lung Cancer Trial Dataset

On CRAN:

Conda:

openblascpp

2.70 score 26 exports 32 dependencies

Last updated from:d0cc4e82cc. Checks:8 ERROR, 2 OK, 3 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR198
linux-devel-x86_64ERROR188
source / vignettesOK260
linux-release-arm64ERROR199
linux-release-x86_64ERROR179
macos-release-arm64ERROR121
macos-release-x86_64ERROR316
macos-oldrel-arm64FAIL91
macos-oldrel-x86_64FAIL327
windows-develERROR280
windows-releaseERROR252
windows-oldrelFAIL57
wasm-releaseOK145

Exports:BASSLINE_convertBF_lambda_obs_LLAPBF_lambda_obs_LLOGBF_lambda_obs_LSTBF_u_obs_LEPCaseDeletion_LEPCaseDeletion_LLAPCaseDeletion_LLOGCaseDeletion_LNCaseDeletion_LSTDIC_LEPDIC_LLAPDIC_LLOGDIC_LNDIC_LSTLML_LEPLML_LLAPLML_LLOGLML_LNLML_LSTMCMC_LEPMCMC_LLAPMCMC_LLOGMCMC_LNMCMC_LSTTrace_plot

Dependencies:clicodacpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMASSMatrixMatrixModelsmcmcMCMCpackmvtnormquantregR6RColorBrewerRcppRcppArmadillorlangS7scalesSparseMsurvivaltruncnormvctrsVGAMviridisLitewithr

Introduction to BASSLINE

Nathan Constantine-Cooke, Catalina Vallejos

Rendered fromBASSLINE.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2022-04-03
Started: 2019-12-28

Readme and manuals

Help Manual

Help pageTopics
Convert dataframe with mixed variables to a numeric matrixBASSLINE_convert
Outlier detection for observation for the log-Laplace modelBF_lambda_obs_LLAP
Outlier detection for observation for the log-logistic modelBF_lambda_obs_LLOG
Outlier detection for observation for the log-student's t modelBF_lambda_obs_LST
Outlier detection for observation for the log-exponential power modelBF_u_obs_LEP
VA Lung Cancer Trial Datasetcancer
Case deletion analysis for the log-exponential power modelCaseDeletion_LEP
Case deletion analysis for the log-Laplace modelCaseDeletion_LLAP
Case deletion analysis for the log-logistic modelCaseDeletion_LLOG
Case deletion analysis for the log-normal modelCaseDeletion_LN
Case deletion analysis for the log-student's t modelCaseDeletion_LST
Deviance information criterion for the log-exponential power modelDIC_LEP
Deviance information criterion for the log-Laplace modelDIC_LLAP
Deviance information criterion for the log-logistic modelDIC_LLOG
Deviance information criterion for the log-normal modelDIC_LN
Deviance information criterion for the log-student's t modelDIC_LST
Log-marginal likelihood estimator for the log-exponential power modelLML_LEP
Log-marginal likelihood estimator for the log-Laplace modelLML_LLAP
Log-marginal likelihood estimator for the log-logistic modelLML_LLOG
Log-marginal Likelihood estimator for the log-normal modelLML_LN
Log-marginal Likelihood estimator for the log-student's t modelLML_LST
MCMC algorithm for the log-exponential power modelMCMC_LEP
MCMC algorithm for the log-Laplace modelMCMC_LLAP
MCMC algorithm for the log-logistic modelMCMC_LLOG
MCMC algorithm for the log-normal modelMCMC_LN
MCMC algorithm for the log-student's t modelMCMC_LST
Produce a trace plot of a variable's MCMC chainTrace_plot