httk.performance {httk}

Historical Performance of R Package httk

Description

This table records the historical performance and other metrics of the R package "httk" as profiled with the function benchmark_httk. There is a row for each version and a column for each benchmark or metric. This table is used to generate graphs comparing the current version to past performance in order to help identify unintended degradtion of package capabilities.

Usage

httk.performance

Format

An object of class data.frame with 23 rows and 18 columns.

Details

Column Name	Description
Version	The release of httk (major.minor.patch)
N.steadystate	The number of chemicals for which Css can be predicted for the steady-state model
calc_analytic.units	The ratio of the output of calc_analytic_css in mg/L to uM multiplied by 1000/MW (should be 1)
calc_mc.units	The ratio of the output of calc_mc_css in mg/L to uM multiplied by 1000/MW (should be 1)
solve_pbtk.units	The ratio of a Cplasma value from solve_pbtk in mg/L to uM multiplied by 1000/MW (should be 1)
RMSLE.Wetmore	Root mean squared log10 error between Css predictions from httk and published values from Wetmore papers (both 95th percentile)
N.Wetmore	Number of chemicals used in RMSLE evaluation
RMSLE.noMC	RMSLE between 95th percentile Css prediction and median prediction
N.noMC	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCss	RMSLE for predictions of in vivo measured Css
N.InVivoCss	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoAUC	RMSLE for predictions of in vivo measured AUCs
N.InVivoAUC	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCmax	RMSLE for predictions of in vivo measured Cmax
N.InVivoCmax	Number of chemicals used in RMSLE evaluation
RMSLE.TissuePC	RMSLE for predicted tissue:plasma partition coefficients
N.TissuePC	Number of chemicals used in RMSLE evaluation
Notes	Why benchmarks/metrics may have changed

References

Davidson-Fritz SE, Evans MV, Chang X, Breen M, Honda GS, Kenyon E, Linakis MW, Meade A, Pearce RG, Purucker T, Ring CL, Sfeir MA, Setzer RW, Sluka JP, Vitense K, Devito MJ, Wambaugh JF (2023). “Transparent and Evaluated Toxicokinetic Models for Bioinformatics and Public Health Risk Assessment.” Unpublished.

See Also

benchmark_httk