Package: lnmixsurv 3.1.7

lnmixsurv: Bayesian Mixture Log-Normal Survival Model

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

Authors:Viviana das Graças Ribeiro Lobo [cre], Thaís Cristina Oliveira da Fonseca [aut], Mariane Branco Alves [aut], Vitor Capdeville [aut], Victor Hugo Soares Ney [aut]

lnmixsurv_3.1.7.tar.gz
lnmixsurv_3.1.7.zip(r-4.7)lnmixsurv_3.1.7.zip(r-4.6)lnmixsurv_3.1.7.zip(r-4.5)
lnmixsurv_3.1.7.tgz(r-4.6-x86_64)lnmixsurv_3.1.7.tgz(r-4.6-arm64)lnmixsurv_3.1.7.tgz(r-4.5-x86_64)lnmixsurv_3.1.7.tgz(r-4.5-arm64)
lnmixsurv_3.1.7.tar.gz(r-4.7-arm64)lnmixsurv_3.1.7.tar.gz(r-4.7-x86_64)lnmixsurv_3.1.7.tar.gz(r-4.6-arm64)lnmixsurv_3.1.7.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
lnmixsurv/json (API)

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

Bug tracker:https://github.com/vivianalobo/lnmixsurv/issues

Pkgdown/docs site:https://vivianalobo.github.io

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • sim_data - Simulated lognormal mixture data.

On CRAN:

Conda:

gslopenblascpp

5.26 score 2 stars 18 scripts 13 downloads 9 exports 51 dependencies

Last updated from:33e76fd694. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK322
linux-devel-x86_64OK348
source / vignettesOK347
linux-release-arm64OK328
linux-release-x86_64OK379
macos-release-arm64OK314
macos-release-x86_64OK639
macos-oldrel-arm64OK611
macos-oldrel-x86_64OK701
windows-develOK395
windows-releaseOK332
windows-oldrelOK354
wasm-releaseFAIL190

Exports:augmentfit_metricsjoin_empirical_hazardnobsplot_fit_on_datasimulate_datasurvival_ln_mixturesurvival_ln_mixture_emtidy

Dependencies:abindbackportsbroomcheckmateclicodetoolscpp11distributionaldplyrfarvergenericsggplot2globalsgluegtablehardhatisobandlabelinglatticelifecyclemagrittrMatrixmatrixStatsnumDerivparsnippillarpkgconfigposteriorprettyunitspurrrR6RColorBrewerRcppRcppArmadilloRcppGSLRcppParallelrlangS7scalessparsevctrsstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Comparison with other models

Rendered fromcompare.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-08
Started: 2023-01-27

Expectation Maximization

Rendered fromexpectation_maximization.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-08
Started: 2024-05-09

Get started

Rendered fromlnmixsurv.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-08
Started: 2023-04-28

Intercept only fits

Rendered fromintercept_only.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-08
Started: 2023-01-27

Parallel computation and posterior analysis

Rendered fromparallel_computation.Rmdusingknitr::rmarkdownon Jun 09 2026.

Last update: 2026-06-08
Started: 2023-08-25

Readme and manuals

Help Manual

Help pageTopics
Augment data with information from a survival_ln_mixture objectaugment.survival_ln_mixture
Augment data with information from a survival_ln_mixture_em objectaugment.survival_ln_mixture_em
Function used to calculate some distance metrics between the predicted survival and the observed survival. 'fit_metrics()' is used to calculate distance metrics between empirical and fitted survival for a predictions object, preferably the object $preds returned from 'plot_fit_on_data()'.fit_metrics
Function used to join the empirical hazard to the datajoin_empirical_hazard
Extract the number of observations from 'survival_ln_mixture' fit.nobs.survival_ln_mixture
Function used to quick visualize the fitted values (survival estimate) on the data used to fit the model (via EM algorithm or Gibbs).plot_fit_on_data
Visualizes the path of the EM algorithmplot.survival_ln_mixture_em
Predict from a Lognormal Mixture Modelpredict.survival_ln_mixture
Predict from a lognormal_em Mixture Model fitted using EM algorithm.predict.survival_ln_mixture_em
Simulated lognormal mixture data.sim_data
Function to simulate survival data from a mixture of normal distribution.simulate_data
Lognormal mixture model - Gibbs samplersurvival_ln_mixture survival_ln_mixture.default survival_ln_mixture.formula
Lognormal mixture model - Expectation-Maximization Algorithmsurvival_ln_mixture_em survival_ln_mixture_em.default survival_ln_mixture_em.formula
Tidying method for a Lognormal Mixture model.tidy.survival_ln_mixture
Tidying method for a Lognormal Mixture model (fitted via Expectation-Maximization algorithm).tidy.survival_ln_mixture_em