Changelog

Source: NEWS.md

survalis 2.4.2

• The "glmnet" learner gains s (passed through spec): predict at "lambda.min" (default, as before), "lambda.1se" or a given penalty value.
• The "ranger" and "orsf" learners now accept transformed terms in the formula, such as log1p(bili) or I(age^2); they failed before with an “illegal column names” error from the engine.

survalis 2.4.1

• cv() and holdout(): a strata column that is not in the data now gives a warning instead of being silently dropped (the resampling is then not stratified). The default strata = "status" already stratifies on the event column of the formula whatever its name (for example cens); the docs now say so.
• listlearners(): the docs now say that a learner with tunable = FALSE, such as "fastgbm", can still be tuned with tune(grid = ).

survalis 2.4.0

• The deprecated underscore names are removed, with no wrappers:

Old	New
list_survlearners()	listlearners()
list_metrics()	listmetrics()
list_interpretability_methods()	listmethods()
group_cv()	groupcv()

• Requires basetable (>= 1.4.5), which renamed as_basetable() to asbasetable().

survalis 2.3.1

• predict(), evaluate() and the other verbs now accept times = 0 (S(0) = 1) in the grid; times must be finite and non-negative. The rpsurv learner fills S(0) = 1 itself because it models log time.

survalis 2.3.0

• Exported names are glued lowercase, like the verbs: listlearners(), listmetrics(), listmethods() and groupcv() replace list_survlearners(), list_metrics(), list_interpretability_methods() and group_cv(). The old names still work with a deprecation warning and will be removed in the next release.

Bug fixes

• coxph predicts with strata() in the formula (one curve per row).
• aftgee fits training data with missing covariates; its default prediction times come from the training data.
• ranger passes case.weights to the engine.
• blackboost keeps mboost’s unrestricted tree tests; trees were stopped early by a Bonferroni test.
• survsvm stores the correct outcome column names.
• densemlp reads its exact time grid; large times were rounded to four digits.
• survmetalearner uses equal weights when NNLS returns all zeros instead of producing NaN.
• plot() of a tuning result draws one line per value of the second tuned parameter instead of joining all configurations.

survalis 2.2.2

• "aftgee" predictions now use the semiparametric baseline of the AFT model: the Kaplan-Meier estimate of the censored residuals log(T) - x’beta, read at log(t) - x’beta. They previously assumed a log-normal error with scale fixed at 1, which miscalibrated survival: on the Rotterdam breast cancer data, mean predicted 5-year survival fell from 0.47 to 0.44 against a Kaplan-Meier estimate of 0.44.
• "bart" predictions between the learner’s coarse time-grid points are now interpolated on the log-survival scale, as for "densemlp" in 2.2.1, instead of holding the value from the start of the interval.

survalis 2.2.1

• "densemlp" predictions between the learner’s coarse time-grid points are now interpolated on the log-survival scale (a constant hazard within each bin) instead of holding the value from the start of the bin. With the default 10 bins the old lookup overstated survival by up to one bin: on the Rotterdam breast cancer data, mean predicted 5-year survival fell from 0.54 to 0.42 against a Kaplan-Meier estimate of 0.44.

survalis 2.2.0

• Removed functions that the verbs replace: benchmark(), benchmark_default_survlearners(), benchmark_tuned_survlearners(), summarise_benchmark(), summarize_benchmark_results(), best_survlearner() and plot_benchmark() (use compare()), estimate_rmst() and plot_rmst() (use estimate()), cv_survmetalearner() (use evaluate(model = "survmetalearner")), plot_survmetalearner_weights(), compute_shap_mean() (use interpret(method = "shap")), plot_survcurve() (use kmplot()) and screen_fdr() (use the "selectcox" or "glmnet" learners).

• New kmplot(): Kaplan-Meier curves in a publication style, with censoring marks, an optional log-rank p-value, optional confidence bands and a number-at-risk table coloured by group. type draws the survival function, the risk function (one minus survival), the cumulative hazard, or the log(-log) survival against log time for checking proportional hazards. A factor status gives the Aalen-Johansen cumulative incidence of the chosen event under competing risks. A fitted model adds its curves, dashed: the mean prediction within each group (model.curve = "predicted", a calibration check) or covariate-adjusted curves by G-computation (model.curve = "adjusted"). Panels combine with ‘patchwork’ operators. survalis now requires ‘ggplot2’ 3.5.0 or later.

Bug fixes

These change results; rerun analyses made with earlier versions.

• Outcome handling: Surv() arguments must now be column names. A computed time or status (Surv(time / 365, status)) was silently read from the raw column and is now an error. A 1/2 or logical status (as in survival::lung) and a status == k recoding now reach every learner and metric as a 0/1 event. fit() drops incomplete rows once, and ~ . works in fit(), predict(), estimate() and the explanation methods.

• Metrics: the C-index now equals survival::concordance(), and the AUC weights cases by the censoring survival just before their event time, as in ‘timeROC’. The IBS no longer depends on the order of times; IAE and ISE integrate over the evaluation window only. Metric values are stored at full precision (rounding happens only when printing), the standard error counts only non-missing splits, and unknown metric names, or single-time metrics given several times, are rejected before any model is fitted.

• Resampling: each split gets its own seed, so evaluate(), compare() and tune() give the same values on repeated runs and whatever ncores is. tune() keeps engine arguments passed in ... in the best spec and rejects an argument that is both tuned and fixed.

• Cox-type predictions: "coxph" now uses survival::survfit(), and the penalized Cox learners use a Breslow baseline on the training linear predictor. The earlier baseline was too large when the mean linear predictor was far from 0.

• Explanations: interpret(method = "shap") uses a permutation estimator whose values add up to the prediction gap; "ale" follows Apley and Zhu (2020), using only the observations inside each bin; "interactions" computes the centred Friedman-Popescu H statistic; "varimp" scores the fixed model on permuted data and reports a positive importance when permuting a feature makes predictions worse, whatever the metric’s direction. Calibration uses newdata when given and handles tied predictions. "shap", "counterfactual" and "surrogate" require a one-row newdata, and the explanation methods use only the model’s own predictors.

• Learners:

  • Tree, forest and network learners ("rsf", "ranger", "cforest", "bart", "blackboost", "densemlp") read their curves as step functions, equal to 1 before the first time.
  • "rpart" uses the Kaplan-Meier curve of each leaf.
  • "nbsurv" predicts each row on its own, avoiding an engine bug in batch prediction.
  • "rsf" follows set.seed().
  • "survmetalearner" stacks on subjects with known status at each landmark, with censoring weights.
  • "xgboost" with objective = "survival:cox" codes censored times as xgboost expects and uses a Breslow baseline. With "survival:aft" it starts from the geometric mean time.
  • "bart" passes ... to BART::surv.bart().
  • "selectcox" carries survival forward past the last follow-up.
  • The design-matrix learners ("glmnet", "coxboost", "xgboost", "aalen", "aftgee") keep the training factor levels, so a single row or a test fold missing a level is coded correctly, and newdata no longer needs the outcome columns.

• kmplot(model = ) accepts a model fitted on a rescaled time (days versus years) and warns when the model does not use the grouping variable.

survalis 2.0.0

• survalis is now built around seven verbs: fit(), predict(), evaluate(), tune(), compare(), interpret() and estimate(). The exported API is these verbs plus the resampling specs (cv(), holdout(), group_cv(), bootstrap()), the lookups (list_survlearners(), list_metrics(), list_interpretability_methods()), Surv() and the veteran data. The granular layer is no longer exported: fit_*(), predict_*(), tune_*(), cv_survlearner(), cv_summary(), cv_plot(), score_survmodel(), compute_*(), plot_*(), the *_survmat() metrics, brier(), survmat_to_*(), default_times(), list_tunable_survlearners(), theme_survalis() and the colour scales. vignette("from-1.0") maps each one to its verb.

• Table operations inside survalis (reshaping, grouped summaries, row binding, sorting and merging) now use ‘basetable’ instead of base R. Result tables stored in fitted objects (evaluate(), compare(), interpret(), estimate()) are basetable objects; as.data.frame() on a survalis result still returns a plain data.frame.

• New decision-weighted scores (dwscore()): the loss of the rule “act if predicted risk exceeds c”, averaged over risk thresholds c with a stated weight (threshold range, SurvAcc, Brier, logarithmic or Beta), with IPCW for censoring. Each score is a weighted area under the net benefit curve. The metric ids "survacc" and "range:<lower>-<upper>" (for example "range:0.05-0.20") work in evaluate(), compare() and tune().

• The "brier" and "ibs" metrics now use the same censoring weights as dwscore() and the usual IPCW Brier score (Graf et al. 1999): events at or before t are weighted by 1 / G(T-). Values can change in the fourth decimal.

• The decision curve (interpret(method = "dca")) now uses the same censoring weights, treats a patient when predicted risk is above the threshold (not equal to it), and computes “treat all” from the same weights instead of the Kaplan-Meier risk. Each "range:<lower>-<upper>" loss then equals the weighted area between the treat-all and model curves exactly.

• Custom learners: model in every verb also accepts list(fit = function(formula, data, ...), predict = function(object, newdata, times)), where predict returns an nrow(newdata) x length(times) matrix of survival probabilities. compare() accepts a list mixing ids and custom learners.

• evaluate(fit, newdata = test) scores a fitted learner on new data (external or temporal validation), and evaluate(S, y = Surv(time, status)) scores a survival matrix produced anywhere.

• as.data.frame() also works on a fit() result (one row: model, engine, formula, n, events; it was an error), and as.data.frame(predict(...), format = "long") gives one row per observation and time (id, time, value). The estimate() result and its summary name the interval bounds conf_low and conf_high (were conf.low and conf.high).

• tune() works for any learner when you supply grid: configurations are scored on shared splits under any resampling spec. Learners listed by list_survlearners(has_tune = TRUE) keep their built-in default grids.

• A grid passed to tune() no longer has to name every parameter of a learner’s built-in tuner. A grid that left one out (for example grid = data.frame(cp = c(0.001, 0.01)) for "rpart") stopped with ‘argument “maxdepth” is missing’. Parameters not in the grid now take the learner’s defaults: such a grid, or one with extra engine arguments, is scored on shared splits like the grid of any other learner, and ... supplies fixed arguments to every configuration. A grid with exactly the built-in columns still uses the built-in tuner. The "survsvm" and "glmnet" tuners also take a data frame grid row by row instead of crossing its columns.

• tune() with a built-in default grid returned no configurations for the "glmnet", "bart", "blackboost", "bnnsurv", "cforest", "flexsurvreg" and "orsf" learners, and refitted the model with none of the tuned parameters, so compare(tune = TRUE) silently compared default fits. Every built-in tuner now returns its scored grid and the refit uses the best configuration. A configuration that fails now gives a warning naming the learner and its parameter values and is left out; if every configuration fails, tune() stops. Before, failed configurations were dropped without a word, or one failure aborted the whole search.

• interpret() gains method = "roc" (time-dependent ROC curve) and method = "dca" (decision curve), both at one eval_time.

• as.data.frame() works on every interpret() result and returns plain column names. It failed for "survcluster", "roc", "dca" and "tree_surrogate" (“cannot coerce class … to a data.frame”), and for "pdp", "ice", "ale" and "calibration" it pasted the parts of the result side by side, recycling the shorter ones, under names such as results.time or integrated.integrated_surv. Now "pdp" and "ice" give one row per feature value, observation and time (feature, feature_value, id, time, surv, type), "ale" one row per feature value and time (feature, feature_value, time, ale), "calibration" the calibration table with its eval_time, "survcluster" one row per observation and time (id, cluster, medoid, time, surv), "roc" and "dca" the curve with its eval_time, and "tree_surrogate" one row per time and feature (splits, r_squared). For "pdp" and "ale", as.data.frame(x, what = "integrated") returns the curve integrated over time (feature, feature_value, integrated). print() shows the first rows of this data frame instead of the raw result list.

• list_survlearners() now returns learner and tunable only, and list_interpretability_methods() returns the interpret() method ids with a one-line summary.

• fit() no longer accepts engine arguments through ...; pass them in spec = list(...). Extra arguments now give an error instead of a warning.

• Fewer silent failures. estimate() warns with the number of failed bootstrap replicates and the first error (and stops if all fail) instead of quietly building the interval from the rest. fit() reports why it could not read a formula. The survival super learner gives the error when it drops a base learner. The blackboost and cforest learners no longer hide engine warnings; cforest caps mtry at the number of predictors itself, as ‘party’ would.

• The stpm2 learner is removed: fit_stpm2() and predict_stpm2() are gone and ‘rstpm2’ is no longer a dependency. Use fit_rpsurv() / fit(model = "rpsurv"), which fits the same Royston-Parmar model on the hazard, odds, or probit scale and adds nonlinear effects (nle).

• The orsf tuner passed its min_events grid column to ‘aorsf’ as n_split, the number of candidate cut points, not the minimum number of events. The column is now split_min_events and sets that minimum.

• The survsvm learner now has its own engine and no longer depends on ‘survivalsvm’. Its “quadprog” path called Matrix::nearPD(), which could stop with “error code 1 from Lapack routine ‘dsyevr’” depending on the BLAS/LAPACK library. The new engine fits a kernel survival support vector regression on log time (squared loss for events, one-sided squared loss for censored times) and solves it exactly by active-set Newton steps with one Cholesky factorization each, so no quadratic programming solver is needed; it is 10 to 50 times faster. Survival curves now come from the Kaplan-Meier estimate of cross-fitted residuals, S(t | x) = S_e(log t - f(x)), which is calibrated, instead of exp(-t / f(x)), which assumed an exponential shape and could give negative times. dist = "lognormal" is new; "exp" and "weibull" remain available. The clinical kernel ("add_kernel") treats a factor as one predictor and accepts constant columns, and "poly_kernel" is fixed. Only type = "regression" is supported (the ranking types give no survival curve); opt.meth and diff.meth are ignored. The default gamma.mu is now 0.1 and the tune_survsvm() grid is c(0.01, 0.1, 1), matched to the new loss scale. The fitted object’s engine is now "survalis".

• The rsf learner failed with “formula is incorrectly specified” for a survival::Surv() outcome, so the survival super learner dropped it. The rsf, orsf, blackboost, bnnsurv and flexsurvreg model objects also recorded the wrong time and status column names.

• The bnnsurv learner (bagged k-nearest-neighbour Kaplan-Meier) now has its own engine and no longer depends on ‘bnnSurvival’, which is unmaintained. ‘bnnSurvival’ predicted through parallel::mclapply(), and forking a session whose multithreaded BLAS (e.g. MKL) is already running could hang predict_bnnsurv(). The new engine runs in the current process and also: one-hot encodes factors (they were integer codes inside the Mahalanobis distance) and matches new data to the training levels; uses the pseudo-inverse covariance, so constant or collinear columns in a bootstrap sample are handled; draws feature subsets from all predictors (a factor counts as one); splits weight equally between neighbours tied at the k-th distance, so results no longer depend on row order; and reads predictions at arbitrary times off the step function instead of interpolating linearly. With numeric predictors and default settings it gives the same curves as ‘bnnSurvival’ for the same seed, about ten times faster. metric = "euclidean" (standardized columns) is new. The fitted object’s engine is now "survalis".

• survalis now declares R (>= 4.4.0) and fastgbm (>= 0.7.0). It claimed R 4.1, but ‘fastgbm’ up to 0.6.1 needed R 4.5.0 and ‘rms’ (imported through ‘pec’) needs R 4.4.0, so installation failed on older R.

• Fitting no longer floods the console with warnings. The glmnet learner passes cox.ties = "breslow", its current default, so ‘glmnet’ 5.0 does not warn on every fit (this gave most of the warnings in a tuning or comparison run). The rpsurv learner muffles the “standard errors unavailable” warning of ‘rpsurv’ 0.8.1, since survalis uses only the predictions. flexsurvreg predictions at a single time no longer warn about an unknown .pred column.

• The survdnn learner is removed, and with it the ‘survdnn’ and ‘torch’ dependencies. ‘torch’ needs a separate LibTorch download that often failed to install. Use the densemlp learner (fit(..., model = "densemlp")), which fits deep survival models with a native C++ backend. fit_survdnn(), predict_survdnn(), and tune_survdnn() are removed.

• survalis no longer defines a plot.formula() method. It replaced graphics::plot.formula() for every package as soon as survalis was loaded (“Registered S3 method overwritten”). plot_survcurve() is no longer exported either: for a Kaplan-Meier curve use plot(survival::survfit()) or a dedicated package such as ‘ggsurvfit’. plot() on a fit() result draws the model’s predicted survival curves.

survalis 1.6.4

Bug fixes found while benchmarking every learner on 31 survival datasets.

• fit_ranger() drops predictors that are constant in the data it is fitted on. With respect.unordered.factors = "order", ranger stopped with “invalid type (NULL) for variable ‘smry$strata’” on a factor with a single level, which happens in tuning folds of data with many rare binary predictors (metabric).
• fit_glmnet() and predict_glmnet() accept a single predictor. glmnet stops with “x should be a matrix with 2 or more columns”, so the predictor matrix is padded with a constant zero column.
• fit_aalen() standardizes numeric covariates and predict_aalen() applies the same scaling. timereg::aalen() silently returned an all-zero fit when covariates were on very different scales, so the predicted survival was 1 for every subject.
• predict_flexsurvreg(), predict_bnnsurv(), and predict_cforest() accept a single evaluation time. A one-time result is now a one-column matrix (it was a one-row matrix, or a flat table for flexsurvreg), so the single-time metrics brier and ece can be computed for these learners.
• fit_survmetalearner() can be called with formula and data only, as benchmark() and compare() do. It then fits the base learners (learners, default coxph, glmnet, rsf), builds out-of-fold predictions, and learns the weights from them. predict_survmetalearner() interpolates the weights when the requested times differ from the fitted grid.
• fit_xgboost() accepts max_depth and eta. tune_xgboost() passed them, so every configuration failed silently and tune() then stopped with an unrelated “Unknown columns: ibs”.
• tune() stops with a clear message when every configuration fails during cross-validation.
• tune_survsvm() has a default param_grid, and tune_survdnn() accepts its own default: an argument check rejected the default because missing() is always true for it.
• Errors raised inside parallel workers (ncores > 1) in compare(), evaluate(), and cv_survlearner() are now reported. functionals::fmapn() returns them as try-error objects, which were dropped, so a failure surfaced as object 'value' not found.

survalis 1.6.3

• Finished the data.table -> basetable migration (1.6.1 landed the shared dt-utils.R choke point; this closes out the rest). All remaining data.table::data.table()/as.data.table()/rbindlist()/melt()/ dcast()/setorderv()/setcolorder()/copy() calls and bare [, list(...), by = ...]/:=/.SD/.N grouped expressions across list_functions.R, plot-survcurve.R, compute_shap.R, compute_shap_mean.R, compute_varimp.R, compute_pdp.R, compute_calibration.R, fit_survmetalearner.R, fit_survdnn.R, fit_bnnsurv.R, fit_coxboost.R, fit_xgboost.R, fit_glmnet.R, fit_survsvm.R, evaluation.R (cv_summary()), survmat-helpers.R (plot_survmat()), and benchmark_default_survlearners.R (summarise_benchmark()/summarize_benchmark_results()/ best_survlearner()) are replaced with base R and basetable::aggregate()/count()/tolong()/towide()/orderrows()/ applyby(). data.table dropped from Imports entirely. cv_summary() and score_survmodel() etc. now return plain data.frames instead of data.tables (no downstream code relied on data.table-specific semantics). data.table::CJ() grid construction is replaced with base expand.grid(). All existing tests pass; one test asserting a "data.table" return class was updated to "data.frame" to match the new contract.

survalis 1.6.2

• Fixed a latent correctness bug in the shared .pmap_rbind_dt()/ .map_rbind_dt() helpers (introduced in 1.6.1’s basetable migration): basetable::rbindfill() silently coerces list-columns (e.g. a per-config vector of hidden-layer sizes) to NA instead of preserving them, unlike data.table::rbindlist(). No current learner grid combined through these helpers carries a list-valued hyperparameter, so this was not an active regression, but it was a landmine for any future one. Both helpers now route through a new internal .rbind_fill_dt() (base R rbind() after padding to a common column set), which preserves list-columns and still handles rows with differing columns (e.g. a failed tuning row that omits metric columns).

survalis 1.6.1

• Migrated the shared R/dt-utils.R internal helpers (.pmap_rbind_dt, .map_rbind_dt, .complete_cases_df, .wide_metric_row, .arrange_by_metric_dt, .select_cols) from data.table to basetable as the row-binding/ordering engine. These helpers are the shared choke point behind every tuner’s parameter-grid results table. Also fixed a latent single-bracket row-filter in tune_bnnsurv() (results[!results$failed]) that only worked under data.table’s DT[i] row-selection semantics; now explicit (results[!results$failed, ]). data.table remains in use elsewhere in the package pending further migration.

survalis 1.6.0

• Bug fix: survmat_to_rmst() (and everything built on it: predict(type = "rmst"), estimate()) previously under-integrated RMST for an off-grid tau – it restricted the time grid to times <= tau and summed whole trapezoidal intervals only, silently dropping the fractional last interval instead of interpolating into it. For example, on a grid of 0:5, tau = 3.5 returned the same value as tau = 3 (wrong). RMST computation is now delegated to tvrmst::rmst_dynamic(), with per-subject linear interpolation to tau on top of its cumulative trapezoidal curve. Values for tau that already land exactly on a grid point are unchanged; values for off-grid tau are now correct. tau beyond the last grid point still holds at the full definite integral (no extrapolation past the observed curve). Added tvrmst to Imports.

survalis 1.5.0

• New learner: nbsurv (conditional naive Bayes survival model, via the nbsurv package). fit_nbsurv()/predict_nbsurv() added (no tuner). Registered in list_survlearners(); 24 learners total.

survalis 1.4.0

• New learner: rpsurv (Royston-Parmar flexible parametric survival, via the rpsurv package). fit_rpsurv()/predict_rpsurv() added (no tuner), restricted cubic splines with optional time-varying covariate effects (tve) and a choice of scale (hazard/odds/normal). Registered in list_survlearners(); 23 learners total.

survalis 1.3.0

Seven-verb interface

• New top-level verbs covering the whole workflow, consistent with the funcml package:
  • fit(formula, data, model, spec, seed) – learner-agnostic fitting; returns a survalis_fit (a subclass of mlsurv_model, so every existing helper keeps working on it).
  • predict(fit, newdata, times, type) – type one of "survival", "risk", "chf", "hazard" (all returning a validated survmat), "rmst", "quantile", "median" (numeric).
  • evaluate(fit_or_formula, times, resampling, metrics) – resampled performance; a survalis_fit reuses its stored recipe.
  • tune(formula, data, model, grid, resampling, metric) – grid search, returns the refit best model as a survalis_fit.
  • compare(formula, data, models, specs, resampling, metrics, tune) – paired multi-model leaderboard on shared folds.
  • interpret(fit, method, newdata, data, times, ...) – single entry point to the compute_* explanation methods, with a plot() that dispatches to the paired helper.
  • estimate(formula, data, model, estimand, treatment, tau) – standardised binary-treatment contrast by G-computation, with a subject bootstrap (refit per replicate by default) and an identification-assumptions note.

Supporting infrastructure

• survmat prediction contract: an n x length(times) numeric matrix carrying its grid in the "times" attribute ("t=<time>" column names kept for display and the legacy metric helpers) and the predicted quantity in "quantity". Missing predictions and non-increasing grids are rejected.
• default_times(): a recorded, data-dependent evaluation grid used whenever times is omitted, so runs stay reproducible.
• Resampling constructors cv() (with repeats), holdout(), group_cv(), bootstrap(); reuse one spec across models for paired comparisons.

New learners

• fastgbm (CRAN): histogram gradient boosting, piecewise-exponential objective by default so survival curves are available at arbitrary times.
• densemlp (CRAN): native-C++ MLP (no torch), IPCW-Brier discrete-time loss by default, predictions interpolated onto the requested times.
• Both registered in list_survlearners(); 22 learners total.

Deprecations

• The granular functions (fit_*(), predict_*(), tune_*(), cv_survlearner(), score_survmodel(), benchmark*(), summarise_benchmark(), estimate_rmst(), …) remain exported and fully supported. They are now marked internal in the manual and emit a one-time per-session note. See ?\survalis-deprecated``. They are scheduled to become unexported in a future release.

survalis 1.2.0

• Added compute_survcluster() and plot_survcluster(): unsupervised clustering of predicted individualized survival curves via unsurv::unsurv() (PAM on a weighted curve representation), registered in list_interpretability_methods(). Added unsurv to Imports and its paper’s citation (El Badisy 2026, <doi:10.1093/bioadv/vbag218>) to Description.

survalis 1.1.0

• Added screen_fdr(): univariate-Cox variable screening with Benjamini-Hochberg FDR control, with a minscreen backfill guarantee.
• Added estimate_rmst() and plot_rmst(): standardized RMST estimation via g-computation, with percentile bootstrap inference. Dispatch between a marginal estimate and a two-arm causal-style contrast is by the trt_col argument (NULL = marginal), matching plot_survmat()’s existing group dispatch convention rather than separate function names.
• Added compute_shap_matrix() and plot_shap_beeswarm(): a beeswarm view of SHAP contributions time-integrated over the full prediction horizon, built on compute_shap()’s existing per-time Shapley decomposition. compute_shap() gained an explicit features argument (defaulting to setdiff(colnames(newdata), c(model$time, model$status))), replacing a hardcoded c("time", "status", "event") exclusion, for consistency with compute_interactions()’s existing features argument.
• Added roc_survmat() (full time-dependent ROC curve, IPCW-weighted like auc_survmat()) and dca_survmat() (decision curve analysis / net benefit for right-censored outcomes, using the same Ĝ(t) IPCW estimator as brier() and auc_survmat()), each with plot()/plot_roc()/plot_dca() methods.

survalis 1.0.0

CRAN release: 2026-08-22

• Added CoxBoost as a new survival learner: fit_coxboost(), predict_coxboost(), and tune_coxboost(), wrapping the CoxBoost package’s likelihood-based componentwise boosting for the Cox partial likelihood. Registered in list_survlearners() and usable directly through benchmark().
• Wrapped tune_glmnet()’s example in \donttest{} (per CRAN win-builder pretest feedback: it exceeded the 5s/10s example runtime limit on Debian/Windows).

survalis 0.9.0

• Added a title argument to every plotting function (plot_ale(), plot_pdp(), plot_shap(), plot_interactions(), plot_calibration(), plot_counterfactual(), plot_surrogate(), plot_tree_surrogate(), plot_varimp(), plot_survmat(), plot_survmetalearner_weights(), plot_benchmark(), cv_plot(), plot_survcurve()). If omitted, the previous automatically generated title is used (fully backward compatible); pass title = NULL to omit the title entirely, for journals that require caption-only figures.

survalis 0.8.9

• Added a dashed zero-reference line to every plot that can show both negative and positive values (previously only plot_shap()’s bar chart had one): plot_ale() (both per_time and integrated), plot_shap()’s time-dependent curve, plot_counterfactual(), plot_surrogate(), and plot_varimp().

survalis 0.8.8

• plot_varimp() now draws a boxplot of the full per-repetition permutation distribution for each feature instead of a single point estimate with no uncertainty shown. compute_varimp() retains the per-repetition values (previously discarded after computing summary stats) as a "raw_scores" attribute on its return value, used by plot_varimp(); falls back to the previous point-plot behavior for objects without that attribute (e.g. hand-built summary tables).

survalis 0.8.7

• Flipped the direction of plot_interactions(type = "heatmap")’s viridis scale so high interaction values are dark and low values are bright, matching the conventional reading of heatmap intensity.

survalis 0.8.6

• Fixed plot_interactions(type = "heatmap"), which was genuinely hard to read: the white -> steelblue gradient had too little contrast in the mid-range, and the diagonal (self-interaction, value 0) blended into the low end of the scale. Switched to scale_fill_viridis_c() (perceptually uniform, high contrast), added an explicit na.value for any genuinely missing cells, and fixed a squished legend by widening the colorbar guide.
• Unified color usage across all plotting functions: grouped plots (color/fill mapped to a variable) previously fell back to ggplot2’s default hue palette everywhere except plot_survcurve(); they now all use scale_color_survalis()/scale_fill_survalis(). Single-series hardcoded colors ("steelblue", "skyblue", "lightblue", "pink", "tomato", plain "blue", and ad-hoc green/red hex codes for positive/negative direction) now draw from the same shared Dark2-based palette instead of arbitrary named/hex colors. This was explicitly deferred in PR #22 and is now closed out.

survalis 0.8.5

• Fixed GitHub Actions CI, which had been failing since before this development cycle: man/compute_tree_surrogate.Rd, man/plot_tree_surrogate.Rd, and man/tune_survdnn.Rd were stale, hand-drifted copies using \donttest{} (which runs under R CMD check --run-donttest, as CI does) instead of the \dontrun{} already present in the roxygen source, causing plot_tree_surrogate(tree_ranger, ...) to fail on an undefined tree_ranger object. Regenerated all three from source via devtools::document(); verified locally with R CMD check --run-donttest (Status: OK), matching the CI configuration.

survalis 0.8.4

• summarise_benchmark() now rounds mean/sd/se/lower/upper to 3 decimals by default (digits = 3 argument), matching cv_summary()’s convention; it previously returned full-precision values (e.g. sd = 0.014142136).

survalis 0.8.3

• Normalized base_size to the shared theme_survalis() default (13) everywhere: compute_shap.R, fit_survmetalearner.R’s weights plot, and plot_benchmark() previously carried over a hardcoded base_size = 14 from before the theme retrofit (PR #22).

survalis 0.8.2

• Migrated compute_shap(), compute_shap_mean(), compute_calibration(), and plot_survmetalearner_weights() from dplyr/tidyr to data.table. This was the remaining unqualified (non-dplyr::-prefixed) dplyr/tidyr usage missed by the original file-by-file migration inventory.
• dplyr, tidyr, purrr, and tibble are now fully removed from Imports: the package no longer depends on any tidyverse package for its data-manipulation code; data.table is the sole engine throughout.

survalis 0.8.1

• Migrated the nested/tuned-CV half of benchmark_default_survlearners.R (benchmark_default_survlearners(), .nested_surv_* helpers, summarise_benchmark(), summarize_benchmark_results(), best_survlearner()) from dplyr/tidyr/tibble to data.table. This completes the file-by-file dplyr/tidyverse -> data.table migration across the package. Reuses the shared .score_metrics() helper in .nested_surv_score_predictions() instead of a fifth duplicated copy of the per-metric scoring switch() block.

survalis 0.8.0

• Migrated cv_survmetalearner() from dplyr/tidyr/purrr/tibble to data.table, reusing the shared .score_metrics() helper instead of a third duplicated copy of the per-metric scoring switch() block.

survalis 0.7.27

• Migrated list_survlearners(), list_interpretability_methods(), and list_metrics() from tibble::tibble() to data.table::data.table() (cosmetic descriptive tables, no behavior change).

survalis 0.7.26

• Migrated plot_survmat() from dplyr/tidyr to data.table (data.table::melt() for the wide-to-long reshape, [.data.table with by = for the group/time summaries).

survalis 0.7.25

• Migrated compute_varimp() from dplyr/purrr/tibble to data.table.

survalis 0.7.24

• Migrated tune_survdnn() from dplyr/tidyr/purrr/tibble to data.table, including the list-valued hidden architecture parameter (grid expansion built manually over unique levels per parameter, since data.table::CJ() doesn’t support list-valued columns).

survalis 0.7.23

• Migrated tune_survsvm() from dplyr/tidyr/purrr/tibble to data.table (grid expansion via data.table::CJ()); the CRAN-motivated refit-fallback behavior (PR #14) is unchanged and still verified passing.

survalis 0.7.22

• Migrated tune_glmnet() from dplyr/tidyr/purrr/tibble to data.table, including its tidyr::crossing() grid-expansion step (now data.table::CJ(), a sorted-unique cross join with matching semantics).

survalis 0.7.21

• Migrated tune_selectcox() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.20

• Migrated tune_flexsurvreg() from dplyr/tidyr/purrr/tibble to data.table. Added .map_rbind_dt() in R/dt-utils.R, a data.table replacement for purrr::map_dfr() over a plain vector (as opposed to .pmap_rbind_dt() for a parameter grid).

survalis 0.7.19

• Migrated tune_blackboost() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.18

• Migrated tune_bart() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.17

• Migrated tune_rsf() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.16

• Migrated tune_orsf() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.15

• Migrated tune_cforest() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.14

• Migrated tune_bnnsurv() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.13

• Migrated tune_xgboost() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.12

• Migrated tune_ranger() from dplyr/tidyr/purrr/tibble to data.table.

survalis 0.7.11

• Fixed benchmark_tuned_survlearners() silently dropping learners whose tune_*() has been migrated to data.table: tuning_results[1, cols, drop = FALSE] relies on data.frame [ semantics, but data.table’s [ does not select columns when cols is a variable (only a literal character vector triggers that); it was returning the raw column-name vector instead of a one-row parameter table, causing every fold to error and the learner to be dropped with a warning. Added .select_cols() in R/dt-utils.R that selects columns correctly for either a data.frame/tibble or a data.table, so the nested-tuning code stays agnostic to migration state.

survalis 0.7.10

• Retrofitted all existing plotting functions to use theme_survalis() instead of ad-hoc theme_minimal() calls with inconsistent base_size values, so package figures share one consistent visual style: plot_ale(), survmat plotting helpers, plot_surrogate()/plot_shap(), cv_plot(), plot_pdp(), plot_interactions(), plot_counterfactual(), plot_tree_surrogate(), plot_benchmark(), plot_varimp(), fit_survmetalearner()’s plotting path. Hardcoded per-plot colors are unchanged in this pass (follow-up).

survalis 0.7.9

• Added plot_survcurve(), a survminer-inspired Kaplan-Meier curve with confidence ribbon, log-rank p-value annotation, and an aligned number-at-risk table, built natively on theme_survalis() / scale_color_survalis() (no dependency on the survminer package). The risk table uses patchwork (added to Suggests).

survalis 0.7.8

• Added theme_survalis(), a shared ggplot2 theme, and scale_color_survalis()/scale_fill_survalis(), colorblind-friendly discrete scales (ColorBrewer “Dark2”-based, recycled/interpolated beyond 8 levels), for a consistent visual style across package figures. Not yet applied to existing plotting functions (follow-up).

survalis 0.7.7

• Migrated tune_rpart() from dplyr/tidyr/purrr/tibble to data.table. Added internal data.table helpers (R/dt-utils.R) shared by the ongoing file-by-file migration of the remaining tune_*()/fit_*() grid-search code (tracked in the project TODO).

survalis 0.7.6

• Added benchmark(), the single entry point for comparing survival learners: tune = FALSE (default) dispatches to benchmark_default_survlearners(), tune = TRUE dispatches to benchmark_tuned_survlearners() (nested CV with per-learner tuning). benchmark_default_survlearners() and benchmark_tuned_survlearners() remain available directly.

survalis 0.7.5

• Migrated the core CV/metrics engine (cv_survlearner(), cv_summary(), score_survmodel()) from dplyr/tidyr/tibble/purrr to data.table; data.table is now the main data-manipulation engine going forward, replacing the dplyr/tidyverse approach used previously. This is a first step; the remaining tuning/fitting code (tune_*()/fit_*()) still uses dplyr/purrr and will be migrated incrementally.
• cv_summary() and score_survmodel()/cv_survlearner() metric values now round to 3 decimals by default (digits = 3 argument on cv_summary()).
• These functions now return data.table objects instead of tibbles (is.data.frame() still holds; code relying on tibble-specific behavior should call tibble::as_tibble() explicitly).

survalis 0.7.4

• Added timeroc_survmat(), a vectorized cumulative/dynamic time-dependent AUC curve over a vector of evaluation times (Uno et al. 2007 / Heagerty and Zheng 2005 estimator), matching timeROC::timeROC(weighting = "marginal") to ~1e-3.
• Fixed auc_survmat() to define cases as events strictly before t_star (time < t_star) rather than time <= t_star, matching the canonical Uno/timeROC definition; this corrects the "auc" metric used throughout score_survmodel(), benchmark_*(), and fit_survmetalearner().
• Added timeROC to Suggests for numerical validation in tests.

survalis 0.7.3

• Fixed plot_pdp() per-time facets using free y-axis scales, which made survival probability panels visually incomparable across facets; now uses a fixed [0, 1] scale via coord_cartesian().

survalis 0.7.2

• Fixed tune_survsvm(refit_best = TRUE) crashing when the top-ranked grid candidate fails to refit on the full dataset (e.g., a quadprog QP infeasibility that only manifests on certain BLAS backends, as seen on CRAN’s OpenBLAS check machine); it now falls back to the next-best candidate and only errors if every candidate fails.

survalis 0.7.1

CRAN release: 2026-04-23

• Revised DESCRIPTION to include CRAN-compliant, family-level methodological references using validated DOI/URL anchors and representative (non-exhaustive) wording.
• Reworked examples across evaluation, benchmarking, interpretation, and meta-learner topics to remove commented code and provide executable minimal examples.
• Replaced unnecessary \\dontrun{} wrappers with \\donttest{} for executable-but-longer workflows and kept fast examples unwrapped where feasible.
• Fixed documentation/example mismatches (including variable-importance examples) and regenerated Rd files with devtools::document().
• Verified package checks including run_dont_test = TRUE for CRAN resubmission readiness.

survalis 0.7.0

• First CRAN submission candidate.
• Added a cumulative/dynamic time-dependent AUC metric to the evaluation layer.
• Added plot_counterfactual() for visualizing counterfactual recommendations.
• Added plot_survmat() for plotting predicted survival curves, including grouped summaries.
• Hardened documentation and examples for CRAN checks.
• Updated dependency declarations so exported learners and CV/tuning helpers work after installation.
• Added clearer runtime checks for survdnn when the LibTorch runtime is missing.