Package index • luna

All functions

add_covariates_to_model(): Wrapper function to add covariates to a pharmpy model

add_table_to_model(): Add new $TABLE record to output variables

calc_condition_number(): Calculate the condition number given a matrix

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_llm_key(): Check presence of LLM API key

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)

compare_nlme_fit(): Compare fit of two or more NLME fits

create_cache(): Create a cache environment to store settings for project and system

create_covariate_search_space(): Create covariate search space definition for pharmpy covsearch

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_pkmodel_search_space(): Create PK mmodel search space definition for pharmpy modelsearch

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.

extract(): Helper function for read_ini

find_file_with_fallback(): Find a file(s) from a model run with a potential fallback

fit_model(): Fit model using NONMEM or nlmixr2

fit_model_nlmixr(): Fit model using nlmixr2

fit_model_nonmem(): Fit model using NONMEM

get_condition_number_for_fit(): Calculate the condition number for a model fit object Performs some safety checks

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_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_luna_config(): Get luna config, either from global config file, or from project config

get_new_run_number(): Get new run number for model fit

get_nmfe_location_for_run(): Helper function to determine nmfe location from various sources The order is as follows:

get_nmfe_output(): Get output from NMFE

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_status(): Get the status of a run

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_time_ago(): Get time to now since a given date, in character

get_time_last_updated_file(): Get date/time stamp for last update to file

get_time_last_updated_folder(): Get a timestamp for when the last update was made to any file in a folder Will look only 1 level deep.

get_tool_from_model(): Get estimation/simulation engine from pharmpy model

get_tools_info(): For a given run, get info on what folders are available for selected Pharmpy or PsN tools

ifelse0(): ifelse function but then based on whether value is NULL or not

insert_entry(): The reverse of pluck_entry, insert_entry() inserts an entry into an unnamded outer list, based on an element in the inner list.

is_luna_cache_available(): Check if luna cache is available / loaded

log_add(): Add event to log

log_get_last_event(): Get last event from log, as list object

luna_cache_get(): Get element from active project Just a convenience wrapper with error checking

luna_compare(): Compare basic run info for 2 or more model runs

luna_config(): Edit luna config file in editor, e.g. RStudio

luna_dataset(): Get data from a model

luna_edit(): Edit model in editor, e.g. RStudio

luna_edit_project(): Edit luna config file in editor, e.g. RStudio

luna_gof(): Creates a ggplot2 panel object with basic goodness of fit plots

luna_help(): Ask AI agent to help

luna_info(): Show info for a model run

luna_list(): List runs in current project

luna_load_project(): Load a project from yaml and gather results. Project is then stored/updated in .luna_cache

luna_log(): Show a log of events (actions and errors)

luna_new_project(): Create a new project folder with template YAML project file

luna_note(): Annotate a run Either add a note or clear notes from a run

luna_project_templates(): Show available templates

luna_run(): Run a NONMEM model

luna_sync(): Synchronize (re-load) luna project

luna_tables(): Get tables from a model / run

luna_tag(): Add a tag to a run Either add a tag or clear tags from a run

luna_tool(): Run external tools (e.g. diagnostics) on models or run outputs This can be used e.g. for bootstraps and VPCs. The function is implemented in a modular way so that it can be easily extended.

luna_xpose(): Read tables for a model and parse into an Xpose database

new_project_from_template(): Create a new project object based on a predefined template

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

pluck_entry(): Pluck an inner list element from an unnamed outer list, where an element matches (default name is "id") a specific value. Analogue to _.pluck() in JS/underscore

print(<luna.run_table>): Prints a list of models / runs in project

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

read_yaml_safe(): Safe way to read YAML

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

runs_as_table(): Convert runs in a project object to a data.frame

save_model_code(): Save model code to a markdown file

save_project(): Save a project to a YAML file

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.

truncate_columns(): Apply column width truncation based on console width and relative width specs

update_cache(): Update luna project cache

validate_id(): Validate id

validate_model(): Validate the specified model, ensure it's valid Pharmpy model