Package index • pharmr.extra

All functions

add_covariates_to_model(): Wrapper function to add covariates to a pharmpy model
add_default_output_tables(): Add one or more default output tables to a model, if they don't already exist in the model.
add_metabolite_compartment(): Add a metabolite compartment, if requested
add_table_to_model(): Add new $TABLE record to output variables
attach_fit_info(): Attach fit info and tables to a fit object, e.g. from model fit or Pharmpy grid search final results
basic_gof_plot(): Create basic goodness of fit plots based on nlmixr2 fit
calc_condition_number(): Calculate the condition number given a matrix
calc_pk_variables(): Calculate some basic PK variables from simulated or observed data
call_nmfe(): Call nmfe
call_pharmpy_fit(): Run model with pharmpy
call_pharmpy_tool(): Generic function for running a pharmpy tool, like bootstrap, or modelsearch. A separate function is available for fit()
call_psn(): Call PsN
change_nonmem_dataset(): Change $DATA in NONMEM model code
check_errors_nm_output(): Detect any errors in the output from NONMEM.
clean_modelfit_data(): Clean / check the dataset before passing to model fitting tool
clean_nonmem_folder(): Remove temporary files from NONMEM run
clean_pharmpy_runfolders(): Clean pharmpy run folders like modelfit1 etc
combine_info_columns(): Combine columns with run info into a data.frame and make sure that rows match (e.g. parameters)
combine_regimens(): Combine several regimens into a single data.frame, which can be passed into run_sim() as regimen argument.
compare_nlme_fit(): Compare fit of two or more NLME fits
compare_nlme_runs(): Compare NLME output from last n runs
create_covariate_search_space(): Create covariate search space definition for pharmpy covsearch
create_dosing_records(): Create dosing records, given a specified regimen as a data frame with potentially multiple regimens and varying dosing times / doses
create_model(): Create model
create_model_nlmixr(): Temporary function that returns a hardcoded nlmixr2 model
create_modelfit_info_table(): Create a data.frame with basic model fit info
create_modelfit_parameter_table(): Create a data.frame with parameter estimates
create_obs_records(): Create observation records, given a specified t_obs vector
create_pharmpy_model_from_list(): Create a model object from the model code and dataset stored as a list object.
create_pkmodel_search_space(): Create PK mmodel search space definition for pharmpy modelsearch
create_regimen(): Create a single regimen
create_run_folder(): Create a folder for a run
create_vpc_data(): Run a simulation based on supplied parameters estimates, and combine into proper format for VPC
detect_nmfe_version(): get nmfe file name from a NONMEM installation folder
estimation_options_defaults: List of default options for estimation method.
export_pharmpy_model(): Export a pharmpy model object.
export_pharmpy_results(): Export a pharmpy model object
extract(): Helper function for read_ini
find_pk_parameter(): Find / match PK parameter based on generic name.
fit_model(): Fit model using NONMEM or nlmixr2
fit_model_nlmixr(): Fit model using nlmixr2
fit_model_nonmem(): Fit model using NONMEM
get_advan(): Get ADVAN number for model
get_compartment_scale(): Get compartment scale definition
get_condition_number_for_fit(): Calculate the condition number for a model fit object Performs some safety checks
get_cov_matrix(): Create a covariance block matrix
get_defined_pk_parameters(): Get all parameters that are defined (from a predefined vector of possible parameters)
get_estimation_options(): Helper function to combine default estimation options with user-specified, and ensure correct format.
get_final_results_from_search(): For a Pharmpy grid search, fetch the fit info and attach to object
get_fit_info(): Get fit info from NONMEM run
get_initial_estimates_from_data(): Get a very crude estimate for V to serve as initial estimate for CL and V, without performing an NCA. The calculation is based on the assumption that often in clinical trial data, there is at least a peak and a trough (and likely other samples) taken, hence it's possible to get a crude estimate for CL and V from that. For 2-compartment models we just set Q and V to half and twice the size of CL and V, which is often a good starting point. In most scenarios this is sufficiently close to the final estimates that estimation methods will be able to find the global minimum.
get_initial_estimates_from_individual_data(): Core function to get parameter estimates from individual data
get_new_run_number(): Get new run number for model fit
get_nmfe_location(): Helper function to determine nmfe location from various sources The order is as follows:
get_nmfe_output(): Get output from NMFE
get_nmtran_from_nmfe(): Get the location of NM-TRAN based on the location of nmfe It's usually up one folder from nmfe, then in tr/NMTRAN.exe
get_obs_compartment(): Get observation compartment number from model
get_ode_size(): Get size of ODE system in $DES
get_parameters_with_iiv(): Get a character vector with all parameters on which IIV is present
get_pharmpy_conf(): Get pharmpy configuration, as an R object (list)
get_pharmpy_runfolders(): Find last pharmpy run folder
get_route_from_data(): Get route from data. If dose and observation events all happen in the same compartment, then assume IV administration, else oral absorption (or sc, im, etc).
get_shrinkage_summary(): Parses a NONMEM output file and extracts shrinkage
get_shrinkage_values(): Get shrinkage values from a single line in NONMEM output
get_tables_from_fit(): Read tables created in model run and return as a list of data.frames
get_tables_from_folder(): Get tables from a folder, by table_names
get_tables_in_model_code(): extract FILE names from $TABLE using simple regex. For some reason the tables are not (yet?) available in pharmpy
get_tool_from_model(): Get estimation/simulation engine from pharmpy model
import_pharmpy_model(): Import a pharmpy model stored using export_pharmpy_model
import_pharmpy_results(): Import pharmpy model results object
is_ltbs_model(): Is the residual error model "log-transform both-sides"?
is_maxeval_zero(): Does the last estimation method in a model have maxeval=0?
nlmixr2_pk_1cmt_iv_linear(): nlmixr2 model: 2-cmt linear model (iv)
nlmixr2_pk_1cmt_oral_linear(): nlmixr2 model: 1-cmt linear model (oral) Linear absorption without lagtime
nlmixr2_pk_1cmt_oral_linear_lag(): nlmixr2 model: 1-cmt linear model (oral) Linear absorption with lagtime
nlmixr2_pk_1cmt_oral_linear_transit(): nlmixr2 model: 1-cmt linear model (oral) Transit compartment absorption model
nlmixr2_pk_2cmt_iv_linear(): nlmixr2 model: 2-cmt linear model (iv)
nlmixr2_pk_2cmt_oral_linear(): nlmixr2 model: 2-cmt linear model (oral)
nlmixr2_pk_2cmt_oral_linear_lag(): nlmixr2 model: 2-cmt linear model (oral) Linear absorption with lagtime
nlmixr2_pk_2cmt_oral_linear_transit(): nlmixr2 model: 2-cmt linear model (oral) Transit compartment absorption model
nm_read_model(): Parse NONMEM model file into a list containing blocks of code
nm_save_dataset(): Save an R data.frame to a NONMEM-style dataset as CSV
nm_save_model(): Write a NONMEM model object to file
nm_update_dataset(): Update $DATA in NONMEM model with new dataset
parse_psn_args(): Parse tool options specified in YAML into PsN commandline args
prepare_run_folder(): Create a folder for running model, with the model and dataset
print(<pharmpy.workflows.results.ModelfitResults>): Print function that provides basic run information for a pharmpy modelfit
print_nmfe_output(): Print nmfe output (stdout and stderr) from a run folder
read_ini(): Read ini file core function
read_table_nm(): NONMEM output table import function
remove_data_section(): Remove $DATA from a NONMEM model
remove_table_from_model(): Remove all $TABLE records from a model
remove_table_sections(): Function to remove all $TABLE sections
remove_tables_from_model(): Remove all $TABLE records from a model
run_nlme(): Run model in NONMEM
run_psn(): Run a PsN tool
run_sim(): Run simulations
run_simulation(): Run simulation from a fitted object
save_model_code(): Save model code to a markdown file
scale_initial_estimates_pk(): Only applies to PK parameters, not all parameters
set_compartment_scale(): Set scaling for certain compartments, e.g. dose and observation compartments.
set_covariance(): Function to set covariance between parameters in the omega block
set_iiv(): Set inter-individual variability on parameters
set_residual_error(): Logic to set the residual error model structure for the model
stack_encounters(): Stack encounters when data from multiple encounters is available for the same ID, and TIME is starting at 0 for each encounter.
update_estimation_method(): Wrapper around pharmr's functions to set/add estimation methods
update_parameters(): Update parameter estimates (and fix)
update_pk_tables(): Updates PK parameter tables (patab)
validate_model(): Validate the specified model, ensure it's valid Pharmpy model