pmsims 1.1.0

New search engine

• A learning-curve engine, method = "curve", is the default in simulate_custom() and the simulate_*() wrappers; the previous engine is available as method = "mlpwr". It fits a monotone learning curve to every replicate simulated so far and puts each new batch where the curve crosses the target, so there is no fixed search range. The answer is a median-unbiased estimate with an interval (diagnostics$curve$n_ci), checked against a shape-free fit (diagnostics$crosscheck_n); targets close to the best achievable performance are flagged. Targets that cannot be reached stop with status "unreachable" and a message saying what is achievable; when 20% or more of replicates fail at every sample size tried, so that no curve can be fitted, it stops with status "replicates_failed".
• plot() shows the learning curve, the target and the answer for method = "curve"; live_plot = TRUE redraws it during the search.
• cores runs the replicates of each batch in parallel; results are the same for any number of cores.
• With method = "curve", when performance levels off within Monte Carlo error of the target, the search stops with status "not_bracketed" and a message giving the performance at both ends of the last doubling, rather than simulating ever larger samples.
• Faster calibration slopes, AUC, concordance, linear predictors and correlated predictors: a binary glm search takes about two thirds of the time, a Cox search about a quarter. Results are unchanged.

Search

• Every stage of the search (start values, the mlpwr and bisection searches, verification and the secondary metric) now evaluates replicates the same way: a model trained on fresh data and scored on a fresh test set. The start-value stage previously scored every replicate against one test set, so it could bracket the wrong range.
• Each replicate has its own random-number stream, so results are reproducible with set.seed(), identical in serial and parallel, and unaffected by how much of the search came before.
• Results are checked before they are returned: the answer is simulated again with fresh replicates (verify_reps) and the result reports a status ("ok", "not_verified", "not_bracketed"). min_n is NA when no answer was found.
• The mlpwr search restarts when its Gaussian-process surrogate fails, instead of erroring.
• The start-value search tries max_n itself as its last rung (it could stop at about half of it).
• When at least half the simulation replicates fail to fit or score the model, the result has status "replicates_failed" and no sample size.
• For calibration slope targets with penalised and machine-learning models (searched on the CSSE scale internally), status messages give calibration slopes.
• parallel = TRUE without cores uses up to 20 cores, as before. Parallel replicates use forked processes, so they run serially on Windows; random forests and xgboost then use one thread per worker.
• With method = "mlpwr", n_reps_total is raised to 2000 for "lm", "glm" and "coxph" models with at most 10 signal parameters, where the Gaussian-process search is limited by its budget. A message says so, and auto_n_reps_total = FALSE keeps the value supplied.
• max_n is the largest sample size the search may try, for every engine. With method = "curve" it caps the whole search (unless max_sample_size is given); with the mlpwr and bisection engines it caps the start-value search, which sets their search range. It defaults to 200,000 for random forests and xgboost and 1,000,000 otherwise.

Breaking changes

• min_n and perf_n are NA when no sample size is found (they were a character message); see status and status_message.
• The simulate_*() wrappers no longer suppress warnings: a result that is not "ok" raises a warning with its status_message, and mlpwr’s own warnings are reported.
• metric_2_at_n is the mean over 10 replicates at min_n (it was one).
• Custom metric functions without a metric attribute count failed replicates as 0.5 (with a message); set attr(metric_function, "value_on_error") to choose another value.

Bug fixes

• Metric values that are not finite (for example, the calibration slope of a lasso that selects no predictors) count as failed replicates.
• Metrics where smaller is better (calibration_in_the_large, brier_score, ibs) are rejected, since the search assumes larger values are better.
• survival_auc computes the time-dependent AUC (cumulative/dynamic, with inverse-probability-of-censoring weights) itself rather than through timeROC, which needs the survival package attached and otherwise left survival_auc returning the concordance index. Results for survival AUC targets change. timeROC is now in Suggests.
• ranger and xgboost thread counts: at least one thread, following option pmsims.threads; xgboost previously used every core.
• Correlated predictors now have the requested pairwise correlation. Results for scenarios with a non-zero correlation (the default is 0.3) will change.
• Complexity 2 and 3 data no longer depend on how many rows are drawn. The nonlinear component now uses fixed constants for each predictor configuration, estimated once from a large reference sample, so training and test sets follow the same outcome model. Results for complexity 2 and 3 change.
• Complexity 4 (Friedman) requires exactly 5 signal predictors and stops with an error otherwise. The Friedman function always uses five predictors, so other values requested a simulation that was not the one performed.
• The binary AUC is computed with cases expected to score higher than controls, so a model that ranks the outcome the wrong way round scores below 0.5. AUC estimates at small sample sizes are lower, so results for binary AUC targets change.
• The binary AUC no longer returns NA on test sets of more than about 90,000 rows (integer overflow).
• ridge continues its lambda path below glmnet’s default end when cross-validation selects the smallest lambda, so its calibration improves with sample size. Results for ridge change, most at large sample sizes.
• Survival lasso and ridge pass cox.ties = "breslow" to glmnet. This is glmnet’s current default, so results are unchanged, but glmnet 5.0 and later warn on every Cox fit that does not set it, and plan to change the default to Efron. glmnet 5.0 or later is now required.
• For survival outcomes, coxph uses the Cox calibration slope, coxph(Surv ~ lp), with metric = "calibration_slope", as it already did with metric = "csse" and as lasso and ridge do. rf and xgboost use the calibration slope at the median event time. Results for coxph calibration slope targets change.

Performance

• Correlated predictors are now drawn directly: equicorrelated normals are transformed to the chosen distribution by its quantile function, replacing the rank-and-sort copula. This is 3-7 times faster for most distributions and dominated runtime for linear models (for example, about 85% of a continuous lm run with 80 predictors).

pmsims 1.0.0

Data-generating mechanisms

• Added four signal-complexity levels: linear, quadratic, quadratic with interactions, and the Friedman benchmark function.
• Added control over nonlinear signal strength, predictor correlation, predictor distribution, and binary predictor prevalence through complexity and data_control.
• Updated binary, continuous, and survival tuning so that calibration uses the same data-generating configuration as the sample-size simulation.

Models and performance metrics

• Added ridge regression across binary, continuous, and survival outcomes and refined the random-forest defaults and XGBoost tuning.
• Added calibration-slope squared error ("csse") for all supported outcome types, defined as -(1 - slope)^2 so that larger is better and 0 is perfect calibration. Unlike the calibration slope itself, it penalises slopes above one as well as below it.
• Routed calibration-slope searches through CSSE for the machine-learning models. The search looks for the sample size at which performance equals the target, which requires the metric to move monotonically with n. The calibration slope does so for classical regression models, which overfit and approach one from below, but not for penalised and tree-based learners, whose slope can approach one from either side depending on how much the fit is shrunk, and so can meet the target at more than one sample size. CSSE is a squared distance from perfect calibration, so it improves monotonically regardless of the direction of miscalibration. Requesting metric = "calibration_slope" with lasso, ridge, rf, or xgboost now searches on the CSSE scale internally and translates the answer back, so targets and results stay on the calibration slope scale.

Sample-size search and results

• Improved adaptive bounds and detection of performance targets that appear unreachable.
• Improved fallback behaviour when model fitting or metric calculation fails.
• Forwarded additional engine arguments consistently through simulate_custom() and the wrapper functions.
• Expanded and standardised the metadata returned by wrapper simulations, which now records the full data-generating configuration alongside the search settings.
• Added an up-front runtime estimate: the adaptive first stage is timed and extrapolated to the full search. Runs expected to take more than 30 minutes report the estimate; runs expected to take more than two hours also warn and ask for confirmation before continuing. The prompt is interactive-only and can be disabled with options(pmsims.confirm_long_runs = FALSE).

Printed output

• Reorganised the console summary into sections: Inputs is now divided into Data-generating scenario, Model and performance, and Simulation, and Results leads with the minimum sample size, followed by the performance expected at that sample size.
• Reported the data-generating configuration under Data-generating scenario: signal complexity, nonlinear strength, predictor distribution, predictor correlation, and binary predictor prevalence.
• Reported performance estimates beneath a Performance at N = ... heading, with the achieved value for the chosen metric shown alongside its target.
• Expressed the sample-size target as a criterion including its direction, for example Calibration slope >= 0.900, using the direction in which the metric improves.
• Replaced internal identifiers with human-readable model names (Random forest rather than rf) and standardised names for statistical quantities (AUC and the C-statistic are both reported as C-statistic for a binary outcome).
• Combined the signal and noise predictor counts onto one line, and stopped repeating the model and mode in Results.
• Moved the mean/assurance explanation and the CSSE footnote below a closing rule, and de-emphasised them.
• Added print(x, verbose = TRUE) for implementation-level detail: internal metric identifiers, the engine settings used for the search, and quantities recorded on an internal search scale. summary() now prints this detailed display.

Interface changes

• Updated the wrapper interface to express predictor configuration through complexity and data_control. The top-level predictor_type and binary_predictor_prevalence arguments have been removed; both are now data_control fields. See the current examples and function documentation for details.
• Candidate predictors are now correlated by default (data_control$correlation = 0.3), whereas 0.5.0 generated independent predictors. Set correlation = 0 to reproduce the earlier behaviour.

pmsims 0.5.0

Initial release

New features

• pmsims introduces a simulation-based framework for minimum sample size estimation in prediction model development.
• The package provides wrapper workflows for binary, continuous, and survival outcomes via simulate_binary(), simulate_continuous(), and simulate_survival().
• These workflows support both mean-based and assurance-based criteria for identifying the smallest sample size that meets a target level of predictive performance.
• A lower-level simulate_custom() interface is available for user-defined data generators, model-fitting functions, and performance metrics.

Experimental machine-learning support

• The wrapper workflows include experimental machine-learning options via regularised regression, random forest, and XGBoost.
• These machine-learning methods have not yet undergone the package’s main validation study and should be treated as experimental in 0.5.0.