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]

# 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:

openblas cpp

2.70 score 26 exports 32 dependencies

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

Target	Result	Time
linux-devel-arm64	ERROR	198
linux-devel-x86_64	ERROR	188
source / vignettes	OK	260
linux-release-arm64	ERROR	199
linux-release-x86_64	ERROR	179
macos-release-arm64	ERROR	121
macos-release-x86_64	ERROR	316
macos-oldrel-arm64	FAIL	91
macos-oldrel-x86_64	FAIL	327
windows-devel	ERROR	280
windows-release	ERROR	252
windows-oldrel	FAIL	57
wasm-release	OK	145

Exports:BASSLINE_convert BF_lambda_obs_LLAP BF_lambda_obs_LLOG BF_lambda_obs_LST BF_u_obs_LEP CaseDeletion_LEP CaseDeletion_LLAP CaseDeletion_LLOG CaseDeletion_LN CaseDeletion_LST DIC_LEP DIC_LLAP DIC_LLOG DIC_LN DIC_LST LML_LEP LML_LLAP LML_LLOG LML_LN LML_LST MCMC_LEP MCMC_LLAP MCMC_LLOG MCMC_LN MCMC_LST Trace_plot

Dependencies:cli coda cpp11 farver ggplot2 glue gtable isoband labeling lattice lifecycle MASS Matrix MatrixModels mcmc MCMCpack mvtnorm quantreg R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales SparseM survival truncnorm vctrs VGAM viridisLite withr

Introduction to BASSLINE

Rendered fromBASSLINE.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Convert dataframe with mixed variables to a numeric matrix	BASSLINE_convert
Outlier detection for observation for the log-Laplace model	BF_lambda_obs_LLAP
Outlier detection for observation for the log-logistic model	BF_lambda_obs_LLOG
Outlier detection for observation for the log-student's t model	BF_lambda_obs_LST
Outlier detection for observation for the log-exponential power model	BF_u_obs_LEP
VA Lung Cancer Trial Dataset	cancer
Case deletion analysis for the log-exponential power model	CaseDeletion_LEP
Case deletion analysis for the log-Laplace model	CaseDeletion_LLAP
Case deletion analysis for the log-logistic model	CaseDeletion_LLOG
Case deletion analysis for the log-normal model	CaseDeletion_LN
Case deletion analysis for the log-student's t model	CaseDeletion_LST
Deviance information criterion for the log-exponential power model	DIC_LEP
Deviance information criterion for the log-Laplace model	DIC_LLAP
Deviance information criterion for the log-logistic model	DIC_LLOG
Deviance information criterion for the log-normal model	DIC_LN
Deviance information criterion for the log-student's t model	DIC_LST
Log-marginal likelihood estimator for the log-exponential power model	LML_LEP
Log-marginal likelihood estimator for the log-Laplace model	LML_LLAP
Log-marginal likelihood estimator for the log-logistic model	LML_LLOG
Log-marginal Likelihood estimator for the log-normal model	LML_LN
Log-marginal Likelihood estimator for the log-student's t model	LML_LST
MCMC algorithm for the log-exponential power model	MCMC_LEP
MCMC algorithm for the log-Laplace model	MCMC_LLAP
MCMC algorithm for the log-logistic model	MCMC_LLOG
MCMC algorithm for the log-normal model	MCMC_LN
MCMC algorithm for the log-student's t model	MCMC_LST
Produce a trace plot of a variable's MCMC chain	Trace_plot