Package: exnexSurv 1.3.0

Victor Ney

exnexSurv: Bayesian EXNEX Models for Survival Analysis in Basket Trials

Implements the Bayesian Exchangeable Non-Exchangeable (EXNEX) framework for right-censored log-normal survival data in basket trials. Based on 'Rcpp' and 'RcppArmadillo', the package provides a fast Gibbs sampler supporting EXNEX, complete pooling, and no pooling models to facilitate methodological comparisons and simulation studies.

Authors:Victor Ney [aut, cre]

exnexSurv_1.3.0.tar.gz
exnexSurv_1.3.0.zip(r-4.7-x86_64)exnexSurv_1.3.0.zip(r-4.7-arm64)exnexSurv_1.3.0.zip(r-4.6-x86_64)exnexSurv_1.3.0.zip(r-4.6-arm64)
exnexSurv_1.3.0.tar.gz(r-4.7-arm64)exnexSurv_1.3.0.tar.gz(r-4.7-x86_64)exnexSurv_1.3.0.tar.gz(r-4.6-arm64)exnexSurv_1.3.0.tar.gz(r-4.6-x86_64)
exnexSurv_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
exnexSurv/json (API)

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

Bug tracker:https://github.com/victorney/exnexsurv/issues

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.44 score 11 scripts 363 downloads 8 exports 16 dependencies

Last updated from:3061685414. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK117
linux-devel-x86_64OK139
source / vignettesOK229
linux-release-arm64OK119
linux-release-x86_64OK138
windows-devel-arm64OK292
windows-devel-x86_64OK173
windows-release-arm64OK274
windows-release-x86_64OK124
wasm-releaseOK116

Exports:compare_waiccompute_waicexnex_survmedian_survivalprobability_superiorityrmstsimulate_datasurvival_curves

Dependencies:backportscheckmatecligluehardhatlifecyclemagrittrpillarpkgconfigRcppRcppArmadillorlangsparsevctrstibbleutf8vctrs

Convergence and Diagnostics
Overview | Simulate data | Fit multiple chains | Traceplots | Running means | Simple numerical diagnostics | Repeat the fit with different seeds | Interpretation
Rendered fromconvergence-and-diagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-09-10
Started: 2026-09-10

Getting Started with exnexSurv
Overview | Simulate a simple dataset | Fit the model with a formula | Run multiple chains in parallel | Fit the model with x and y | Fit a model without covariates | Basic posterior summaries | Inspecting the resolved priors | Notes
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-10
Started: 2026-09-10

Running Chains in Parallel
Overview | Simulate data | Fit two chains in parallel | Fit more chains than workers | Traceplots with all chains | Compare to sequential execution | Practical notes
Rendered fromparallel-chains.Rmdusingknitr::rmarkdown

Last update: 2026-09-10
Started: 2026-09-10

The EXNEX Model, Priors, and Data Augmentation
Introduction | The model | Priors and how to customize them | Model variants | Data augmentation | The Gibbs sampler | What is fitted and what is returned | Worked example: selective borrowing | Customizing the non-exchangeable prior per basket | Comparing EXNEX, EX, and No pooling | Performance and validation | References
Rendered frommodel-and-methods.Rmdusingknitr::rmarkdown

Last update: 2026-09-10
Started: 2026-09-10

Tools for Experimenters
A quick simulated example | Survival curves | Posterior median survival time | Restricted mean survival time (RMST) | Model comparison with WAIC | Probability that one group beats another
Rendered fromtools-for-experimenters.Rmdusingknitr::rmarkdown

Last update: 2026-09-10
Started: 2026-09-10