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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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