A B C D E F G H I J K L M O P R S T W
add_chemtable | Add a table of chemical information for use in making httk predictions. |
age_draw_smooth | Draws ages from a smoothed distribution for a given gender/race combination |
apply_clint_adjustment | Correct the measured intrinsive hepatic clearance for fraction free |
apply_fup_adjustment | Correct the measured fraction unbound in plasma for lipid binding |
armitage_estimate_sarea | Estimate well surface area |
armitage_eval | Evaluate the updated Armitage model |
armitage_input | Armitage et al. (2014) Model Inputs from Honda et al. (2019) |
augment.table | Add a parameter value to the chem.physical_and_invitro.data table |
available_rblood2plasma | Find the best available ratio of the blood to plasma concentration constant. |
Aylward2014 | Aylward et al. 2014 |
aylward2014 | Aylward et al. 2014 |
benchmark_httk | Assess the current performance of httk relative to historical benchmarks |
blood_mass_correct | Find average blood masses by age. |
blood_weight | Predict blood mass. |
bmiage | CDC BMI-for-age charts |
body_surface_area | Predict body surface area. |
bone_mass_age | Predict bone mass |
brain_mass | Predict brain mass. |
calc_analytic_css | Calculate the analytic steady state plasma concentration. |
calc_analytic_css_1comp | Calculate the analytic steady state concentration for the one compartment model. |
calc_analytic_css_3comp | Calculate the analytic steady state concentration for model 3comp |
calc_analytic_css_3compss | Calculate the analytic steady state concentration for the three compartment steady-state model |
calc_analytic_css_pbtk | Calculate the analytic steady state plasma concentration for model pbtk. |
calc_css | Find the steady state concentration and the day it is reached. |
calc_dow | Calculate the distribution coefficient |
calc_elimination_rate | Calculate the elimination rate for a one compartment model |
calc_fabs.oral | Functions for calculating the bioavaialble fractions from oral doses |
calc_fbio.oral | Functions for calculating the bioavaialble fractions from oral doses |
calc_fetal_phys | Calculate maternal-fetal physiological parameters |
calc_fgut.oral | Functions for calculating the bioavaialble fractions from oral doses |
calc_fup_correction | Calculate the correction for lipid binding in plasma binding assay |
calc_half_life | Calculates the half-life for a one compartment model. |
calc_hepatic_clearance | Calculate the hepatic clearance (deprecated). |
calc_hep_bioavailability | Calculate first pass heaptic metabolism |
calc_hep_clearance | Calculate the hepatic clearance. |
calc_hep_fu | Calculate the free chemical in the hepaitic clearance assay |
calc_ionization | Calculate the ionization. |
calc_kair | Calculate air:matrix partition coefficients |
calc_krbc2pu | Back-calculates the Red Blood Cell to Unbound Plasma Partition Coefficient |
calc_ma | Calculate the membrane affinity |
calc_maternal_bw | Calculate maternal body weight |
calc_mc_css | Distribution of chemical steady state concentration with uncertainty and variability |
calc_mc_oral_equiv | Calculate Monte Carlo Oral Equivalent Dose |
calc_mc_tk | Conduct multiple TK simulations using Monte Carlo |
calc_rblood2plasma | Calculate the constant ratio of the blood concentration to the plasma concentration. |
calc_stats | Calculate toxicokinetic summary statistics (deprecated). |
calc_tkstats | Calculate toxicokinetic summary statistics. |
calc_total_clearance | Calculate the total plasma clearance. |
calc_vdist | Calculate the volume of distribution for a one compartment model. |
CAS.checksum | Test the check digit of a CAS number to confirm validity |
chem.invivo.PK.aggregate.data | Parameter Estimates from Wambaugh et al. (2018) |
chem.invivo.PK.data | Published toxicokinetic time course measurements |
chem.invivo.PK.summary.data | Summary of published toxicokinetic time course experiments |
chem.physical_and_invitro.data | Physico-chemical properties and in vitro measurements for toxicokinetics |
ckd_epi_eq | CKD-EPI equation for GFR. |
concentration_data_Linakis2020 | Concentration data involved in Linakis 2020 vignette analysis. |
convert_httkpop_1comp | Converts HTTK-Pop physiology into parameters relevant to the one compartment model |
convert_solve_x | convert_solve_x |
convert_units | convert_units |
create_mc_samples | Create a table of parameter values for Monte Carlo |
Dawson2021 | Dawson et al. 2021 data |
dawson2021 | Dawson et al. 2021 data |
EPA.ref | Reference for EPA Physico-Chemical Data |
estimate_gfr | Predict GFR. |
estimate_gfr_ped | Predict GFR in children. |
estimate_hematocrit | Generate hematocrit values for a virtual population |
example.seem | SEEM Example Data We can grab SEEM daily intake rate predictions already in RData format from https://github.com/HumanExposure/SEEM3RPackage/tree/main/SEEM3/data Download the file Ring2018Preds.RData |
example.toxcast | ToxCast Example Data The main page for the ToxCast data is here: https://www.epa.gov/comptox-tools/exploring-toxcast-data Most useful to us is a single file containing all the hits across all chemcials and assays: https://clowder.edap-cluster.com/datasets/6364026ee4b04f6bb1409eda?space=62bb560ee4b07abf29f88fef |
export_pbtk_jarnac | Export model to jarnac. |
export_pbtk_sbml | Export model to sbml. |
fetalPCs | Fetal Partition Coefficients |
fetalpcs | Fetal Partition Coefficients |
Frank2018invivo | Literature In Vivo Data on Doses Causing Neurological Effects |
gen_age_height_weight | Generate demographic parameters for a virtual population |
gen_height_weight | Generate heights and weights for a virtual population. |
gen_serum_creatinine | Generate serum creatinine values for a virtual population. |
get_caco2 | Retrieve in vitro measured Caco-2 membrane permeabilit |
get_cheminfo | Retrieve chemical information available from HTTK package |
get_chem_id | Retrieve chemical identity from HTTK package |
get_clint | Retrieve and parse intrinsic hepatic clearance |
get_fabsgut | Retrieve or calculate fraction of chemical absorbed from the gut |
get_fup | Retrieve and parse fraction unbound in plasma |
get_gfr_category | Categorize kidney function by GFR. |
get_invitroPK_param | Retrieve species-specific in vitro data from chem.physical_and_invitro.data table |
get_lit_cheminfo | Get literature Chemical Information. |
get_lit_css | Get literature Css |
get_lit_oral_equiv | Get Literature Oral Equivalent Dose |
get_physchem_param | Get physico-chemical parameters from chem.physical_and_invitro.data table |
get_rblood2plasma | Get ratio of the blood concentration to the plasma concentration. |
get_weight_class | Assign weight class (underweight, normal, overweight, obese) |
get_wetmore_cheminfo | Get literature Chemical Information. (deprecated). |
get_wetmore_css | Get literature Css (deprecated). |
get_wetmore_oral_equiv | Get Literature Oral Equivalent Dose (deprecated). |
hct_h | KDE bandwidths for residual variability in hematocrit |
hematocrit_infants | Predict hematocrit in infants under 1 year old. |
honda.ivive | Return the assumptions used in Honda et al. 2019 |
honda2023.data | Measured Caco-2 Apical-Basal Permeability Data |
honda2023.qspr | Predicted Caco-2 Apical-Basal Permeabilities |
howgate | Howgate 2006 |
httk.performance | Historical Performance of R Package httk |
httkpop | httkpop: Virtual population generator for HTTK. |
httkpop_biotophys_default | Convert HTTK-Pop-generated parameters to HTTK physiological parameters |
httkpop_direct_resample | Generate a virtual population by directly resampling the NHANES data. |
httkpop_direct_resample_inner | Inner loop function called by 'httkpop_direct_resample'. |
httkpop_generate | Generate a virtual population for PBTK |
httkpop_mc | httk-pop: Correlated human physiological parameter Monte Carlo |
httkpop_virtual_indiv | Generate a virtual population by the virtual individuals method. |
hw_H | KDE bandwidth for residual variability in height/weight |
in.list | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
invitro_mc | Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability. |
is.expocast | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.httk | Convenience Boolean (yes/no) function to identify chemical membership and treatment within the httk project. |
is.nhanes | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.blood.analyte | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.blood.parent | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.serum.analyte | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.serum.parent | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.urine.analyte | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.nhanes.urine.parent | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.pharma | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.tox21 | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is.toxcast | Convenience Boolean (yes/no) functions to identify chemical membership in several key lists. |
is_in_inclusive | Checks whether a value, or all values in a vector, is within inclusive limits |
johnson | Johnson 2006 |
Kapraun2019 | Kapraun et al. 2019 data |
kapraun2019 | Kapraun et al. 2019 data |
kidney_mass_children | Predict kidney mass for children |
liver_mass_children | Predict liver mass for children |
load_dawson2021 | Load CLint and Fup QSPR predictions from Dawson et al. 2021. |
load_honda2023 | Load Caco2 QSPR predictions from Honda et al. 2023 |
load_pradeep2020 | Load CLint and Fup QSPR predictions predictions from Pradeep et al. 2020. |
load_sipes2017 | Load CLint and Fup QSPR predictions from Sipes et al 2017. |
lump_tissues | Lump tissue parameters into model compartments |
lung_mass_children | Predict lung mass for children |
mcnally_dt | Reference tissue masses and flows from tables in McNally et al. 2014. |
mecdt | Pre-processed NHANES data. |
metabolism_data_Linakis2020 | Metabolism data involved in Linakis 2020 vignette analysis. |
monte_carlo | Monte Carlo for toxicokinetic model parameters |
Obach2008 | Published Pharmacokinetic Parameters from Obach et al. 2008 |
onlyp | NHANES Exposure Data |
pancreas_mass_children | Predict pancreas mass for children |
parameterize_1comp | Parameters for a one compartment (empirical) toxicokinetic model |
parameterize_3comp | Parameters for a three-compartment toxicokinetic model (dynamic) |
parameterize_fetal_pbtk | Parameterize_fetal_PBTK |
parameterize_gas_pbtk | Parameters for a generic gas inhalation physiologically-based toxicokinetic model |
parameterize_pbtk | Parameters for a generic physiologically-based toxicokinetic model |
parameterize_schmitt | Parameters for Schmitt's (2008) Tissue Partition Coefficient Method |
parameterize_steadystate | Parameters for a three-compartment toxicokinetic model at steady-state |
pc.data | Partition Coefficient Data |
Pearce2017Regression | Pearce et al. 2017 data |
pearce2017regression | Pearce et al. 2017 data |
pharma | DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data |
physiology.data | Species-specific physiology parameters |
pksim.pcs | Partition Coefficients from PK-Sim |
Pradeep2020 | Pradeep et al. 2020 |
pradeep2020 | Pradeep et al. 2020 |
predict_partitioning_schmitt | Predict partition coefficients using the method from Schmitt (2008). |
pregnonpregaucs | AUCs for Pregnant and Non-Pregnant Women |
propagate_invitrouv_1comp | Propagates uncertainty and variability in in vitro HTTK data into one compartment model parameters |
propagate_invitrouv_3comp | Propagates uncertainty and variability in in vitro HTTK data into three compartment model parameters |
propagate_invitrouv_pbtk | Propagates uncertainty and variability in in vitro HTTK data into PBPK model parameters |
reset_httk | Reset HTTK to Default Data Tables |
rfun | Randomly draws from a one-dimensional KDE |
rmed0non0u95 | Draw random numbers with LOD median but non-zero upper 95th percentile |
r_left_censored_norm | Returns draws from a normal distribution with a lower censoring limit of lod (limit of detection) |
scale_dosing | Scale mg/kg body weight doses according to body weight and units |
scr_h | KDE bandwidths for residual variability in serum creatinine |
set_httk_precision | set_httk_precision |
Sipes2017 | Sipes et al. 2017 data |
sipes2017 | Sipes et al. 2017 data |
skeletal_muscle_mass | Predict skeletal muscle mass |
skeletal_muscle_mass_children | Predict skeletal muscle mass for children |
skin_mass_bosgra | Predict skin mass |
solve_1comp | Solve one compartment TK model |
solve_3comp | Solve_3comp |
solve_fetal_pbtk | Solve_fetal_PBTK |
solve_gas_pbtk | solve_gas_pbtk |
solve_model | Solve_model |
solve_pbtk | Solve_PBTK |
spleen_mass_children | Predict spleen mass for children |
supptab1_Linakis2020 | Supplementary output from Linakis 2020 vignette analysis. |
supptab2_Linakis2020 | More supplementary output from Linakis 2020 vignette analysis. |
Tables.Rdata.stamp | A timestamp of table creation |
tissue.data | Tissue composition and species-specific physiology parameters |
tissue_masses_flows | Given a data.table describing a virtual population by the NHANES quantities, generates HTTK physiological parameters for each individual. |
tissue_scale | Allometric scaling. |
wambaugh2019 | in vitro Toxicokinetic Data from Wambaugh et al. (2019) |
wambaugh2019.nhanes | NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019) |
wambaugh2019.raw | Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019) |
wambaugh2019.seem3 | ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates |
wambaugh2019.tox21 | Tox21 2015 Active Hit Calls (EPA) |
Wang2018 | Wang et al. 2018 Wang et al. (2018) screened the blood of 75 pregnant women for the presence of environmental organic acids (EOAs) and identified mass spectral features corresponding to 453 chemical formulae of which 48 could be mapped to likely structures. Of the 48 with tentative structures the identity of six were confirmed with available chemical standards. |
wang2018 | Wang et al. 2018 Wang et al. (2018) screened the blood of 75 pregnant women for the presence of environmental organic acids (EOAs) and identified mass spectral features corresponding to 453 chemical formulae of which 48 could be mapped to likely structures. Of the 48 with tentative structures the identity of six were confirmed with available chemical standards. |
well_param | Microtiter Plate Well Descriptions for Armitage et al. (2014) Model |
Wetmore2012 | Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012. |
wfl | WHO weight-for-length charts |