Package: lnmixsurv 3.1.6

Victor Hugo Soares Ney

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 [aut], Thaís Cristina Oliveira da Fonseca [aut], Mariane Branco Alves [aut], Vitor Capdeville [aut], Victor Hugo Soares Ney [cre]

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lnmixsurv.pdf |lnmixsurv.html✨
lnmixsurv/json (API)

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

Peer review:

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

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:

6.16 score 2 stars 18 scripts 121 downloads 9 exports 64 dependencies

Last updated 30 days agofrom:0cb677613a. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-win-x86_64NOTENov 23 2024
R-4.5-linux-x86_64NOTENov 23 2024
R-4.4-win-x86_64NOTENov 23 2024
R-4.4-mac-x86_64NOTENov 23 2024
R-4.4-mac-aarch64NOTENov 23 2024
R-4.3-win-x86_64NOTENov 23 2024
R-4.3-mac-x86_64NOTENov 23 2024
R-4.3-mac-aarch64NOTENov 23 2024

Exports:augmentfit_metricsjoin_empirical_hazardnobsplot_fit_on_datasimulate_datasurvival_ln_mixturesurvival_ln_mixture_emtidy

Dependencies:abindbackportsbitbit64broomcheckmateclicliprcodetoolscolorspacecpp11crayondistributionaldplyrfansifarvergenericsggplot2globalsgluegtablehardhathmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivparsnippillarpkgconfigposteriorprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadilloRcppGSLRcppParallelreadrrlangscalesstringistringrsurvivaltensorAtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr

Comparison with other models

Rendered fromcompare.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2024-10-24
Started: 2023-01-27

Expectation Maximization

Rendered fromexpectation_maximization.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2024-08-23
Started: 2024-05-09

Get started

Rendered fromlnmixsurv.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2024-08-23
Started: 2023-04-28

Intercept only fits

Rendered fromintercept_only.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2024-08-23
Started: 2023-01-27

Parallel computation and posterior analysis

Rendered fromparallel_computation.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2024-08-23
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