Package: survalis 0.7.1

survalis: Interpretable Survival Machine Learning Framework

A modular toolkit for interpretable survival machine learning with a unified interface for fitting, prediction, evaluation, and interpretation. It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Royston and Parmar (2002) <doi:10.1002/sim.1203>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>, Jaeger et al. (2019) <doi:10.1214/19-AOAS1261>, Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Graf et al. (1999) <doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>, Friedman (2001) <doi:10.1214/aos/1013203451>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, and Lundberg and Lee (2017) <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>, and other related methods for survival modeling, prediction, and interpretation.

Authors:Imad El Badisy [aut, cre]

survalis_0.7.1.tar.gz
survalis_0.7.1.zip(r-4.7)survalis_0.7.1.zip(r-4.6)survalis_0.7.1.zip(r-4.5)
survalis_0.7.1.tgz(r-4.6-any)survalis_0.7.1.tgz(r-4.5-any)
survalis_0.7.1.tar.gz(r-4.7-any)survalis_0.7.1.tar.gz(r-4.6-any)
survalis_0.7.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
survalis/json (API)

# Install 'survalis' in R:

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

Bug tracker:https://github.com/ielbadisy/survalis/issues

Datasets:

veteran - Veteran's Administration Lung Cancer Trial Data

On CRAN:

interpretable-machine-learning survival-analysis

3.98 score 3 scripts 267 downloads 99 exports 174 dependencies

Last updated from:44df45d1ae. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	264
source / vignettes	OK	264
linux-release-x86_64	OK	268
macos-release-arm64	OK	228
macos-oldrel-arm64	OK	197
windows-devel	OK	226
windows-release	OK	169
windows-oldrel	OK	205
wasm-release	OK	174

Exports:auc_survmat benchmark_default_survlearners benchmark_tuned_survlearners best_survlearner brier cindex_survmat compute_ale compute_calibration compute_counterfactual compute_interactions compute_pdp compute_shap compute_surrogate compute_tree_surrogate compute_varimp cv_plot cv_summary cv_survlearner cv_survmetalearner ece_survmat fit_aalen fit_aftgee fit_bart fit_blackboost fit_bnnsurv fit_cforest fit_coxph fit_flexsurvreg fit_glmnet fit_orsf fit_ranger fit_rpart fit_rsf fit_selectcox fit_stpm2 fit_survdnn fit_survmetalearner fit_survsvm fit_xgboost iae_survmat ibs_survmat ise_survmat list_interpretability_methods list_metrics list_survlearners list_tunable_survlearners plot_ale plot_benchmark plot_calibration plot_counterfactual plot_interactions plot_pdp plot_shap plot_surrogate plot_survmat plot_survmetalearner_weights plot_tree_surrogate plot_varimp predict_aalen predict_aftgee predict_bart predict_blackboost predict_bnnsurv predict_cforest predict_coxph predict_flexsurvreg predict_glmnet predict_orsf predict_ranger predict_rpart predict_rsf predict_selectcox predict_stpm2 predict_survdnn predict_survmetalearner predict_survsvm predict_xgboost score_survmodel summarise_benchmark summarize_benchmark_results Surv survmat_to_chf survmat_to_haz survmat_to_quantile survmat_to_rmst tune_bart tune_blackboost tune_bnnsurv tune_cforest tune_flexsurvreg tune_glmnet tune_orsf tune_ranger tune_rpart tune_rsf tune_selectcox tune_survdnn tune_survsvm tune_xgboost

Dependencies:aftgee aorsf assertthat backports BART base64enc BB bbmle bdsmatrix bit bit64 bnnSurvival broom bslib cachem callr checkmate cli clipr cluster cmprsk codetools coin collapse colorspace coro cpp11 crayon data.table data.tree desc deSolve diagram DiagrammeR digest doParallel dplyr evaluate farver fastGHQuad fastmap flexsurv fontawesome foreach foreign Formula fs functionals furrr future future.apply geepack generics ggplot2 glmnet globals glue gower gridExtra gtable highr Hmisc hms htmlTable htmltools htmlwidgets igraph inum isoband iterators jquerylib jsonlite kernlab KernSmooth knitr labeling lattice lava libcoin lifecycle listenv lsoda magrittr MASS Matrix MatrixModels matrixStats mboost memoise mets mgcv mime modeltools mstate muhaz multcomp mvtnorm nlme nnet nnls numDeriv otel parallelly party partykit pec pillar pkgconfig plotrix polspline pracma prettyunits processx prodlim progress progressr ps Publish purrr quadprog quantreg R6 randomForestSRC ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen readr riskRegression rlang rmarkdown rms rpart rsample rstpm2 rstudioapi S7 safetensors sandwich sass scales shape slider SparseM SQUAREM stabs statmod stringi stringr strucchange survdnn survival survivalsvm TH.data tibble tidyr tidyselect timereg tinytex torch tzdb utf8 vctrs viridisLite visNetwork vroom warp withr xfun xgboost yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Time-Dependent AUC from a Survival-Probability Matrix	auc_survmat
Benchmark Multiple Survival Learners (Cross-Validation Wrapper)	benchmark_default_survlearners
Benchmark Tuned Survival Learners with Nested Cross-Validation	benchmark_tuned_survlearners
Select the Best Survival Learner by a Given Metric	best_survlearner
Brier Score with IPCW for a Single Time Point	brier
Concordance Index from a Survival-Probability Matrix	cindex_survmat
Accumulated Local Effects (ALE) for Survival Models	compute_ale
Calibration of Survival Predictions at a Single Time Point	compute_calibration
Compute individual counterfactual changes to increase survival	compute_counterfactual
Compute Feature Interactions for Survival Predictions	compute_interactions
Partial Dependence and ICE for Survival Predictions	compute_pdp
Compute local SHAP-like contributions for survival predictions	compute_shap
Local Surrogate Explanation for Survival Predictions (LIME-style)	compute_surrogate
Compute Tree-Based Surrogate Model for Survival Predictions	compute_tree_surrogate
Permutation variable importance for survival models	compute_varimp
Boxplot of Cross-Validation Metric Distributions	cv_plot
Summarize Cross-Validation Results	cv_summary
Cross-Validate a Survival Learner (fold-mapped with 'fmapn')	cv_survlearner
Cross‑Validate a Stacked Survival Meta‑Learner	cv_survmetalearner
Expected Calibration Error (ECE) at a Single Time Point	ece_survmat
Fit an Additive Hazards (Aalen) Model	fit_aalen
Fit an Accelerated Failure Time Model Using Generalized Estimating Equations	fit_aftgee
Fit a Bayesian Additive Regression Trees (BART) Survival Model	fit_bart
Fit a Componentwise Gradient Boosted Cox Model (blackboost)	fit_blackboost
Fit a kNN–Ensemble Survival Model (bnnSurvival)	fit_bnnsurv
Fit a Conditional Inference Survival Forest	fit_cforest
Fit a Cox Proportional Hazards Model	fit_coxph
Fit a Parametric Survival Regression Model Using flexsurvreg	fit_flexsurvreg
Fit a Penalized Cox Proportional Hazards Model (glmnet)	fit_glmnet
Fit an Oblique Random Survival Forest (ORSF) Model	fit_orsf
Fit a Survival Random Forest Model Using ranger	fit_ranger
Fit a Survival Tree Model using 'rpart'	fit_rpart
Fit a Random Survival Forest (RSF) Model	fit_rsf
Fit a Predictor-Selection Cox Model (pec::selectCox, mlsurv_model-compatible)	fit_selectcox
Fit a Flexible Parametric Survival Model (rstpm2, mlsurv_model-compatible)	fit_stpm2
Fit a Deep Neural Network Survival Model (mlsurv_model-compatible)	fit_survdnn
Fit a Stacked Survival Meta‑Learner (Time‑Varying NNLS)	fit_survmetalearner
Fit a Survival SVM Model (mlsurv_model-compatible)	fit_survsvm
Fit an XGBoost Survival Model (mlsurv_model-compatible)	fit_xgboost
Integrated Absolute Error Against Kaplan-Meier	iae_survmat
Integrated Brier Score (Discrete Integration)	ibs_survmat
Integrated Squared Error Against Kaplan-Meier	ise_survmat
List interpretability methods available in survalis	list_interpretability_methods
List Available Evaluation Metrics	list_metrics
List survival learners available in survalis	list_survlearners
List tunable survival learners	list_tunable_survlearners
Plot ALE Curves for Survival Models	plot_ale
Plot Benchmark Distributions Across Learners	plot_benchmark
Plot Calibration Curve for Survival Predictions	plot_calibration
Plot Counterfactual Recommendations	plot_counterfactual
Plot Interaction Strengths for Survival Models	plot_interactions
Plot PDP/ICE Curves for Survival Models	plot_pdp
Plot SHAP-like contributions for survival models	plot_shap
Plot Local Surrogate Explanation	plot_surrogate
Plot Predicted Survival Curves from a survmat	plot_survmat
Plot Time‑Varying Stacking Weights	plot_survmetalearner_weights
Plot Tree-Based Surrogate Models or Feature Importances	plot_tree_surrogate
Plot Permutation Variable Importance	plot_varimp
Predict Survival from an Aalen Additive Hazards Model	predict_aalen
Predict Survival Probabilities from an 'aftgee' Model	predict_aftgee
Predict Survival Probabilities from a BART Survival Model	predict_bart
Predict Survival Probabilities from a blackboost Model	predict_blackboost
Predict Survival with a bnnSurvival Model	predict_bnnsurv
Predict Survival Probabilities from a Conditional Inference Survival Forest	predict_cforest
Predict Survival Probabilities from a Cox PH Model	predict_coxph
Predict Survival Probabilities from a flexsurvreg Model	predict_flexsurvreg
Predict Survival Probabilities from a Penalized Cox Model (glmnet)	predict_glmnet
Predict Survival Probabilities from an ORSF Model	predict_orsf
Predict Survival Probabilities from a ranger Model	predict_ranger
Predict Survival Probabilities from an 'rpart' Survival Tree	predict_rpart
Predict Survival Probabilities from an RSF Model	predict_rsf
Predict Survival Probabilities with a Selected Cox Model	predict_selectcox
Predict Survival Probabilities with an rstpm2 Model	predict_stpm2
Predict Survival Probabilities with a DNN Survival Model	predict_survdnn
Predict with a Stacked Survival Meta‑Learner	predict_survmetalearner
Predict Survival Probabilities with Survival SVM	predict_survsvm
Predict Survival with XGBoost	predict_xgboost
Score a Fitted Survival Model on Its Training Data	score_survmodel
Summarise Benchmark Results (Mean SD with Wald CI)	summarise_benchmark
Compact Table of Mean SD by Learner and Metric	summarize_benchmark_results
Summarize an 'mlsurv_model'	summary.mlsurv_model
Convert a survival-probability matrix (survmat) to cumulative hazard	survmat_to_chf
Convert a survival-probability matrix (survmat) to hazards on a time grid	survmat_to_haz
Compute a survival-time quantile from a survival-probability matrix (survmat) with grid-based approach	survmat_to_quantile
Compute restricted mean survival time (RMST) from a survival-probability matrix (survmat)	survmat_to_rmst
Tune BART Survival Hyperparameters (Cross-Validation)	tune_bart
Tune blackboost Hyperparameters (Cross-Validation)	tune_blackboost
Tune bnnSurvival Hyperparameters (Cross-Validation)	tune_bnnsurv
Tune a Conditional Inference Survival Forest	tune_cforest
Tune Parametric Survival Models with flexsurvreg	tune_flexsurvreg
Tune Penalized Cox Proportional Hazards Model via Cross-Validation	tune_glmnet
Tune Oblique Random Survival Forests (ORSF) via Cross-Validation	tune_orsf
Hyperparameter Tuning for ranger Survival Models	tune_ranger
Tune a Survival Tree Model ('rpart') via Cross-Validation	tune_rpart
Tune Random Survival Forest Hyperparameters (Cross-Validation)	tune_rsf
Tune SelectCox Rule (Cross-Validation)	tune_selectcox
Tune Deep Neural Network Survival Models (Cross-Validation)	tune_survdnn
Tune Survival SVM Hyperparameters (Cross-Validation)	tune_survsvm
Tune XGBoost Survival Hyperparameters (Cross-Validation)	tune_xgboost
Veteran's Administration Lung Cancer Trial Data	veteran