Package: spsurv 1.1.0

spsurv: Bernstein Polynomial Based Semiparametric Survival Analysis

Semiparametric survival analysis based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <doi:10.48550/arXiv.2003.10548>.

Authors:Renato Panaro [aut, cre, cph], Fábio Demarqui [ths, rev], Vinicius Mayrink [ths]

spsurv_1.1.0.tar.gz
spsurv_1.1.0.zip(r-4.7-x86_64)spsurv_1.1.0.zip(r-4.7-arm64)spsurv_1.1.0.zip(r-4.6-x86_64)spsurv_1.1.0.zip(r-4.6-arm64)spsurv_1.1.0.zip(r-4.5-x86_64)
spsurv_1.1.0.tgz(r-4.6-x86_64)spsurv_1.1.0.tgz(r-4.6-arm64)spsurv_1.1.0.tgz(r-4.5-x86_64)spsurv_1.1.0.tgz(r-4.5-arm64)
spsurv_1.1.0.tar.gz(r-4.7-arm64)spsurv_1.1.0.tar.gz(r-4.7-x86_64)spsurv_1.1.0.tar.gz(r-4.6-arm64)spsurv_1.1.0.tar.gz(r-4.6-x86_64)
spsurv_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spsurv/json (API)

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

Bug tracker:https://github.com/rvpanaro/spsurv/issues

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

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

bernstein-polynomialrstansurvival-analysissurvival-regressiononetbbcpp

6.72 score 5 stars 44 scripts 286 downloads 22 exports 52 dependencies

Last updated from:ee5461d3e3. Checks:13 OK, 2 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK259
linux-devel-x86_64OK327
source / vignettesOK444
linux-release-arm64OK262
linux-release-x86_64OK247
macos-release-arm64OK284
macos-release-x86_64ERROR447
macos-oldrel-arm64OK195
macos-oldrel-x86_64ERROR471
windows-devel-arm64OK559
windows-devel-x86_64OK296
windows-release-arm64OK396
windows-release-x86_64OK299
windows-oldrel-x86_64OK307
wasm-releaseOK229

Exports:as_draws_df.spbpbernsteinbp_survival_regbp.basisbpaftbpphbppocredintestimatesggresidualsproportional_oddspw.basisrank_modelssespbpspread_surv_draws.spbpspsurv_fit_proportional_hazardsspsurv_fit_proportional_oddsspsurv_fit_survival_regspsurv_pred_linear_predspsurv_pred_survivalspsurv_pred_time

Dependencies:abindbackportsBHcallrcheckmateclicodacpp11descdistributionalfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMatrixmatrixStatsnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanS7scalesStanHeaderssurvivaltensorAtibbleutf8vctrsviridisviridisLitewithr

Bayesian analysis with Stan
Before you model | What to decide | Fit and summarize | Visual check — posterior forest plot | Visual check — MLE vs Bayes | Why this is insightful | Credible intervals and criteria | Convergence (optional deep dive) | tidybayes posterior draws | Survival prediction
Rendered frombayesian-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-09-08
Started: 2026-07-12

Choosing PH, PO, and AFT models
Before you model | Why this is insightful | What to decide | Fit and summarize | Visual check — survival overlay | When to use which family
Rendered frommodel-families.Rmdusingknitr::rmarkdown

Last update: 2026-09-08
Started: 2026-07-12

Getting started with spsurv
Before you model | Why this is insightful | What to decide | Fit and summarize | Specifying the Bernstein degree | The spbp object | Visual check — KM (step) vs Bernstein survival (smooth) | Visual check — forest plot | Printing | Next steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-08
Started: 2026-07-12

Survival prediction and ggplot workflows
Before you model | Why this is insightful | What to decide | Fit and summarize | Prediction grid | predict() — tidy data frame | Visual check — KM vs model overlay | survfit() with tidy = TRUE | Bayesian fits | Interval types (MLE)
Rendered fromsurvival-ggplot.Rmdusingknitr::rmarkdown

Last update: 2026-09-08
Started: 2026-07-12

tidymodels and tidybayes integration
parsnip engines | workflows | Convenience constructor | Censored predictions from spbp fits | Bayesian: tidybayes
Rendered fromtidymodels.Rmdusingknitr::rmarkdown

Last update: 2026-07-12
Started: 2026-07-12

Bernstein polynomial degree and baseline plots
Before you model | Why this is insightful | What to decide | Fit and summarize | Visual check — baseline overlay | Gamma information stability | Basis functions
Rendered frombp-degree.Rmdusingknitr::rmarkdown

Last update: 2026-07-12
Started: 2026-07-12

Inference and model comparison (MLE)
Before you model | Fit and summarize | Visual check — forest plot | Visual check — model comparison | Why this is insightful | What to decide | Degrees of freedom (visual) | Confidence intervals and variance | estimates(), se(), and extractAIC() | Null model
Rendered frommle-inference.Rmdusingknitr::rmarkdown

Last update: 2026-07-12
Started: 2026-07-12

Model checking and residuals
Before you model | Why this is insightful | What to decide | Fit and summarize | Visual check — martingale residuals | Visual check — Cox-Snell calibration | Visual check — deviance residuals | Compare with Cox (reference) | Gamma information stability | Bayesian fits
Rendered fromdiagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-07-12
Started: 2026-07-12

Readme and manuals

Help Manual

Help pageTopics
The 'spsurv' package.spsurv-package spsurv
Akaike information criterion for fitted spbp modelsAIC.spbp
Analysis of deviance table for nested spbp modelsanova.spbp
Convert spbp Bayesian fit to posterior drawsas_draws_df.spbp
Augment data with spbp model informationaugment.spbp
Bernstein polynomial baseline specificationbernstein
Bernstein survival regression specificationbp_survival_reg
Bernstein basis polynomials calculationsbp.basis
Bernstein AFT Modelbpaft
Bernstein PH Modelbpph
Bernstein PO Modelbppo
Estimated regression coefficientscoef.spbp
Confidence intervals for the regression coefficientsconfint.spbp
Generic S3 method credintcredint
Credible intervals for the regression coefficientscredint.spbp
Parameter estimates for fitted spbp modelsestimates
Extract AIC from a fitted spbp modelextractAIC.spbp
ggplot2 residual diagnostic plots for spbp modelsggresiduals
Glance at a fitted spbp modelglance.spbp
Log-likelihood for fitted spbp modelslogLik.spbp
Model.matrix method for fitted spbp modelsmodel.matrix.spbp
BP based models plot.plot.spbp
Predicted survival as a tidy data framepredict.spbp
Bernstein Polynomial Based Regression Object Printprint.spbp
Bernstein Polynomial Based Regression Object Summary BPAFT Bayesprint.summary.bpaft.bayes
Bernstein Polynomial Based Regression Object Summary BPAFT MLEprint.summary.bpaft.mle
Bernstein Polynomial Based Regression Object Summary BPPH Bayesprint.summary.bpph.bayes
Bernstein Polynomial Based Regression Object Summary BPPH MLEprint.summary.bpph.mle
Bernstein Polynomial Based Regression Object Summary BPPO Bayesprint.summary.bppo.bayes
Bernstein Polynomial Based Regression Object BPPO MLEprint.summary.bppo.mle
Bernstein Polynomial Based Regression Object Summary Bayesprint.summary.spbp.bayes
Bernstein Polynomial Based Regression Object Summary MLEprint.summary.spbp.mle
Proportional odds regression model specificationproportional_odds
Power basis polynomials calculationspw.basis
Rank fitted spbp models by AICrank_models
BP based models residuals.residuals.spbp
Standard errors for fitted spbp model parametersse
spbp: The BP Based Survival Analysis Functionspbp
spbp: The BP Based Semiparametric Survival Analysis Functionspbp.default
Posterior survival curves in long tidy formatspread_surv_draws.spbp
Bernstein Polynomial Based Regression Object Summarysummary.spbp
BP-based model survival curvesas.data.frame.survfitbp plot.survfitbp survfit.spbp
Tidy summary for fitted spbp modelstidy.spbp
Covariance of the regression coefficientsvcov.spbp