Package index • mipdeval

All functions
`accuracy()` `is_accurate()` `is_accurate_abs()` `is_accurate_rel()`	Accuracy
`add_grouping_column()`	Add grouping column using a function
`bootstrap_metrics()` `summarise_bootstrap_metrics()` `summarize_bootstrap_metrics()`	Calculate metrics across bootstrapped folds
`bootstrap_options()`	Options for bootstrapping error metrics
`calc_eta()`	Calculate the "eta"-value for a parameters, assuming an exponential shape for IIV, i.e. `PAR = TV_PAR * EXP(ETA(n))`, and additive shape for IOV (kappa's), i.e. `PAR = TV_PAR + ETA(n)` or when final parameter estimate is 0.
`calculate_bayesian_impact()`	Calculate the impact of using Bayesian updating compared to population estimates
`calculate_bootstrap_summ()`	Bootstrap confidence intervals for forecasting error metrics
`calculate_shrinkage()`	Calculate eta-shrinkage
`calculate_stats()`	Calculate basic statistics, like RMSE, MPE, MAPE for forecasted data
`check_failed_fits()`	Check for failed fits / predictions and (optionally) warn
`check_input_data()`	Do some checks and minor manipulations on input dataset
`check_installed_model_library()`	Check if PKPDsim model library is installed
`compare_psn_proseval_results()` `reldiff_psn_proseval_results()` `compare_psn_execute_results()` `reldiff_psn_execute_results()`	Compare mipdeval results with PsN
`fit_options()`	Options for controlling MAP Bayesian fit
`get_omega_for_parameters()`	Return all diagonal om^2 elements for each non-fixed parameter, as a list
`get_required_covariates()`	Get required covariates from a PKPDsim model object
`group_by_dose()`	Will create a separate group for each dose intervals that contains at least one sample
`group_by_time()`	Group data by time using bin separators
`handle_covariate_censoring()`	Handle covariate censoring
`handle_sample_weighting()`	Handle weighting of samples
`is_timevarying()`	For requested columns in a dataset, check if values vary or not across rows
`mape()`	Mean absolute percentage error
`mpe()`	Mean percentage error
`nm_busulfan`	Busulfan data
`nm_vanco`	Vancomycin data
`nrmse()`	Normalized root-mean-squared error
`parse_input_data()`	Parse NONMEM-style input data, prepare for main eval loop
`parse_model()`	Parse PKPDsim model information
`parse_psn_proseval_results()`	Parse PsN::proseval results.csv to filter out only the rows that we need (for prediction of next sample or group of samples)
`plot(<mipdeval_results>)`	Plot method for a `run_eval()` object
`print(<mipdeval_results>)`	Print results from run_eval()
`print(<mipdeval_results_bayesian_impact>)`	Print Bayesian impact results from run_eval()
`print(<mipdeval_results_shrinkage>)`	Print shrinkage results from run_eval()
`print(<mipdeval_results_stats_summ>)`	Print predictive performance statistics from run_eval()
`read_input_data()`	Read input data
`rmse()`	Root-mean-squared error
`run_eval()`	Run iterative predictive analysis, looping over each individual's data
`run_eval_core()`	Core iterative simulation and MAP estimation function that loops over an individual's dataset
`run_vpc_core()`	Core function for creating visual predictive checks (VPCs). Runs `n_samples` simulations for a single subject
`ss()`	Weighted sum-of-squares of residuals
`stats_summ_options()`	Options for summary statistics
`vec_assert_or_null()`	Assert an argument has known prototype and/or size or is NULL
`vpc_options()`	Options for VPC simulations

Reference

All functions