This package is considered a duplicate. The official version of this package is found at:https://rvpanaro.r-universe.dev/spsurv

Package: spsurv 1.1.0

Renato Panaro

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.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://cran.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:

4.25 score 44 scripts 382 downloads 22 exports 52 dependencies

Last updated from:a0b58bd8da. Checks:10 OK. Indexed: no.

Target	Result	Time	Files	Syslog
linux-devel-arm64	OK	254
linux-devel-x86_64	OK	239
source / vignettes	OK	421
linux-release-arm64	OK	287
linux-release-x86_64	OK	321
windows-devel-arm64	OK	395
windows-devel-x86_64	OK	419
windows-release-arm64	OK	397
windows-release-x86_64	OK	293
wasm-release	OK	184

Exports:as_draws_df.spbp bernstein bp_survival_reg bp.basis bpaft bpph bppo credint estimates ggresiduals proportional_odds pw.basis rank_models se spbp spread_surv_draws.spbp spsurv_fit_proportional_hazards spsurv_fit_proportional_odds spsurv_fit_survival_reg spsurv_pred_linear_pred spsurv_pred_survival spsurv_pred_time

Dependencies:abind backports BH callr checkmate cli coda cpp11 desc distributional farver generics ggplot2 glue gridExtra gtable inline isoband labeling lattice lifecycle loo magrittr Matrix matrixStats numDeriv otel pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan S7 scales StanHeaders survival tensorA tibble utf8 vctrs viridis viridisLite withr

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

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: 2026-09-28
Started: 2026-09-28

tidymodels and tidybayes integration

parsnip engines | workflows | Convenience constructor | Censored predictions from spbp fits | Bayesian: tidybayes

Rendered fromtidymodels.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-28

Citation

Readme and manuals

Help Manual

Help page	Topics
The 'spsurv' package.	spsurv-package spsurv
Akaike information criterion for fitted spbp models	AIC.spbp
Analysis of deviance table for nested spbp models	anova.spbp
Convert spbp Bayesian fit to posterior draws	as_draws_df.spbp
Augment data with spbp model information	augment.spbp
Bernstein polynomial baseline specification	bernstein
Bernstein survival regression specification	bp_survival_reg
Bernstein basis polynomials calculations	bp.basis
Bernstein AFT Model	bpaft
Bernstein PH Model	bpph
Bernstein PO Model	bppo
Estimated regression coefficients	coef.spbp
Confidence intervals for the regression coefficients	confint.spbp
Generic S3 method credint	credint
Credible intervals for the regression coefficients	credint.spbp
Parameter estimates for fitted spbp models	estimates
Extract AIC from a fitted spbp model	extractAIC.spbp
ggplot2 residual diagnostic plots for spbp models	ggresiduals
Glance at a fitted spbp model	glance.spbp
Log-likelihood for fitted spbp models	logLik.spbp
Model.matrix method for fitted spbp models	model.matrix.spbp
BP based models plot.	plot.spbp
Predicted survival as a tidy data frame	predict.spbp
Bernstein Polynomial Based Regression Object Print	print.spbp
Bernstein Polynomial Based Regression Object Summary BPAFT Bayes	print.summary.bpaft.bayes
Bernstein Polynomial Based Regression Object Summary BPAFT MLE	print.summary.bpaft.mle
Bernstein Polynomial Based Regression Object Summary BPPH Bayes	print.summary.bpph.bayes
Bernstein Polynomial Based Regression Object Summary BPPH MLE	print.summary.bpph.mle
Bernstein Polynomial Based Regression Object Summary BPPO Bayes	print.summary.bppo.bayes
Bernstein Polynomial Based Regression Object BPPO MLE	print.summary.bppo.mle
Bernstein Polynomial Based Regression Object Summary Bayes	print.summary.spbp.bayes
Bernstein Polynomial Based Regression Object Summary MLE	print.summary.spbp.mle
Proportional odds regression model specification	proportional_odds
Power basis polynomials calculations	pw.basis
Rank fitted spbp models by AIC	rank_models
BP based models residuals.	residuals.spbp
Standard errors for fitted spbp model parameters	se
spbp: The BP Based Survival Analysis Function	spbp
spbp: The BP Based Semiparametric Survival Analysis Function	spbp.default
Posterior survival curves in long tidy format	spread_surv_draws.spbp
Bernstein Polynomial Based Regression Object Summary	summary.spbp
BP-based model survival curves	as.data.frame.survfitbp plot.survfitbp survfit.spbp
Tidy summary for fitted spbp models	tidy.spbp
Covariance of the regression coefficients	vcov.spbp

Usage by other packages (reverse dependencies)