Package: poissonsuperlearner 0.2.0

Gabriele Pittarello

poissonsuperlearner: Poisson Super Learner

Provides tools for fitting piecewise-constant hazard models for survival and competing risks data, including ensemble hazard estimation via the Super Learner framework. The package supports estimation of survival functions and absolute risk predictions from fitted cause-specific hazard models. For the Super Learner framework see van der Laan, Polley and Hubbard (2007) <doi:10.2202/1544-6115.1309>.

Authors:Gabriele Pittarello [aut, cre], Helene Rytgaard [aut], Thomas Gerds [aut]

poissonsuperlearner_0.2.0.tar.gz
poissonsuperlearner_0.2.0.tar.gz(r-4.7-arm64)poissonsuperlearner_0.2.0.tar.gz(r-4.7-x86_64)poissonsuperlearner_0.2.0.tar.gz(r-4.6-arm64)poissonsuperlearner_0.2.0.tar.gz(r-4.6-x86_64)
poissonsuperlearner_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
poissonsuperlearner/json (API)

# Install 'poissonsuperlearner' in R:

install.packages('poissonsuperlearner', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.00 score 3 scripts 503 downloads 10 exports 101 dependencies

Last updated from:032b486e2c. Checks:6 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	234
linux-devel-x86_64	OK	248
source / vignettes	OK	250
linux-release-arm64	OK	231
linux-release-x86_64	OK	209
wasm-release	OK	177

Exports:fit_learner Learner_gam Learner_glmnet Learner_hal pch_absolute_risk pch_absolute_risk_euler pch_survival predictRisk.poisson_superlearner simulateStenoT1 Superlearner

Dependencies:backports base64enc bslib cachem checkmate cli cluster cmprsk codetools colorspace cpp11 data.table diagram digest doParallel evaluate farver fastmap fontawesome foreach foreign Formula fs future future.apply ggplot2 glmnet globals glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lpSolve magrittr MASS Matrix MatrixModels memoise mets mgcv mime multcomp mvtnorm nlme nnet numDeriv parallelly plotrix polspline prodlim progressr Publish quantreg R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rms rpart rstudioapi S7 sampling sandwich sass scales shape SparseM SQUAREM stringi stringr survival TH.data timereg tinytex vctrs viridisLite withr xfun yaml zoo

Basic Usage

Introduction | Data | One Learner Library For All Causes | One Learner Library Per Cause

Rendered frombasicusage.Rmdusingknitr::rmarkdown

Last update: 2026-05-18
Started: 2026-04-04

Help page	Topics
Extract coefficients from a fitted base learner	coef.base_learner
Extract stacking (meta-learner) coefficients from a fitted Poisson Super Learner	coef.poisson_superlearner
Fit a single base learner	fit_learner
GAM learner via 'mgcv::bam'	Learner_gam Learner_gam-class
Penalized Poisson learner via 'glmnet'	Learner_glmnet Learner_glmnet-class
HAL learner for piecewise Poisson hazards	Learner_hal Learner_hal-class
Absolute risk (cumulative incidence) for a cause under piecewise-constant hazards	pch_absolute_risk
Absolute risk (Euler approximation) for a cause under piecewise-constant hazards	pch_absolute_risk_euler
Piecewise-constant hazards survival function	pch_survival
Predict hazards, survival and absolute risk from a fitted base learner	predict.base_learner
Predict hazards, survival and absolute risk from a fitted Poisson Super Learner	predict.poisson_superlearner
Absolute-risk matrix predictions for a fitted base learner	predictRisk.base_learner
Absolute-risk matrix predictions for a fitted Poisson Super Learner	predictRisk.poisson_superlearner
Print method for 'base_learner'	print.base_learner
Print method for 'poisson_superlearner'	print.poisson_superlearner
Simulate time-to-event data for hypothetical type-1 diabetes patients	simulateStenoT1
Summarize a fitted base learner object	summary.base_learner
Summarize a fitted Poisson Super Learner object	summary.poisson_superlearner
Fit a Poisson Super Learner ensemble	Superlearner

Package: poissonsuperlearner 0.2.0

poissonsuperlearner: Poisson Super Learner

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)