Package index • mrgsolve

All functions

PARAM() FIXED() THETA() INIT() CMT() CAPTURE() HANDLEMATRIX(): Functions to parse code blocks

PKMODEL(): Parse PKMODEL BLOCK data

Req() req(): Request simulated output

aboutsolver: About the lsoda differential equation solver used by mrgsolve

as.ev(): Coerce an object to class ev

as.list(<mrgmod>): Coerce a model object to list

as.list(<mrgsims>): Coerce an mrgsims object to list

as_data_set(): Create a simulation data set from ev objects or data frames

as_deslist(): Create a list of designs from a data frame

blocks(): Return the code blocks from a model specification file

carry_out() carry.out(): Select items to carry into simulated output

check_data_names(): Check input data set names against model parameters

cmtn(): Get the compartment number from a compartment name

code(): Extract the code from a model

collapse_omega() collapse_sigma(): Collapse OMEGA or SIGMA matrix lists

collapse_matrix(): Collapse the matrices of a matlist object

custom_tol() custom_rtol() custom_atol(): Customize tolerances for specific compartments

data_set(): Select and modify a data set for simulation

design(): Set observation designs for the simulation

details(): Extract model details

env_eval(): Re-evaluate the code in the ENV block

env_get() env_get_env(): Return model environment

env_ls(): List objects in the model environment

env_update(): Update objects in model environment

ev(): Event objects for simulating PK and other interventions

ev_assign() assign_ev(): Replicate a list of events into a data set

ev_days(): Schedule dosing events on days of the week

mutate(<ev>) select(<ev>) filter(<ev>): dplyr verbs for event objects

`$`(<ev>) `[[`(<ev>): Select columns from an ev object

ev_rep(): Replicate an event object

ev_repeat(): Repeat a block of dosing events

ev_rx() parse_rx(): Create intervention objects from Rx input

ev_seq() seq(<ev>): Schedule a series of event objects

evd() as.evd(): Create an event object with data-like names

exidata extran1 extran2 extran3 exTheoph exBoot: Example input data sets

expand.idata() expand.ev() expand.evd() ev_expand() evd_expand(): Create template data sets for simulation

expand_observations(): Insert observations into a data set

get_tol() get_tol_list(): Extract rtol and atol information from a model object

idata_set(): Select and modify a idata set for simulation

init(): Methods for working with the model compartment list

inventory(): Check whether all required parameters needed in a model are present in an object

is.mrgmod(): Check if an object is a model object

is.mrgsims(): Check if an object is mrgsims output

lctran() uctran(): Change the case of nmtran-like data items

loadso(): Load the model shared object

c(<matlist>): Operations with matlist objects

as_bmat() as_dmat() as_cmat(): Coerce R objects to block or diagonal matrices

bmat() cmat() dmat(): Create matrices from vector input

mcRNG(): Set RNG to use L'Ecuyer-CMRG

mcode() mcode_cache(): Write, compile, and load model code

modlib(): Internal model library

modlib_details: modlib: PK/PD Model parameters, compartments, and output variables

modlib_pk: modlib: Pharmacokinetic models

modlib_pkpd: modlib: Pharmacokinetic / pharmacodynamic models

modlib_tmdd: modlib: Target mediated disposition model

modlib_viral: modlib: HCV viral dynamics models

mread() mread_cache() mread_file(): Read a model specification file

mread_yaml() yaml_to_cpp(): Read a model from yaml format

`$`(<mrgmod>) `[[`(<mrgmod>) `[`(<mrgmod>): Select parameter values from a model object

mrgsim() mrgsim_df() do_mrgsim(): Simulate from a model object

mrgsim_q(): Simulate from a model object with quicker turnaround

mrgsim_e() mrgsim_d() mrgsim_ei() mrgsim_di() mrgsim_i() mrgsim_0(): mrgsim variant functions

pull(<mrgsims>) filter(<mrgsims>) group_by(<mrgsims>) distinct(<mrgsims>) mutate(<mrgsims>) summarise(<each>) summarise(<mrgsims>) do(<mrgsims>) select(<mrgsims>) slice(<mrgsims>) as_data_frame.mrgsims() as_tibble(<mrgsims>) as.tbl.mrgsims(): Methods for handling output with dplyr verbs

mutate_sims() select_sims() filter_sims(): Methods for modifying mrgsims objects

mrgsolve mrgsolve-package: mrgsolve: Simulate from ODE-Based Models

mwrite_cpp(): Write a model to native mrgsolve format

mwrite_yaml(): Write model code to yaml format

names(<mrgmod>): Get all names from a model object

nmext(): Import model estimates from a NONMEM ext file

nmxml(): Import model estimates from a NONMEM xml file

numerics_only(): Prepare data.frame for input to mrgsim()

obsaug(): Augment observations in the simulated output

obsonly(): Collect only observation records in the simulated output

omat(): Manipulate OMEGA matrices

outvars(): Show names of current output variables

param() allparam(): Create and work with parameter objects

param_tags(): Return parameter tags

plot(<batch_mrgsims>,<missing>) plot(<batch_mrgsims>,<formula>): Plot method for mrgsims objects

plot(<mrgsims>,<missing>) plot(<mrgsims>,<formula>) plot(<mrgsims>,<character>): Generate a quick plot of simulated data

plot_sims(): Plot data as an mrgsims object

qsim(): Basic, simple simulation from model object

read_nmext(): Extract estimates from NONMEM ext file

realize_addl(): Make addl doses explicit in an event object or data set

render() dorender(): Render a model to a document

reserved(): Reserved words

reset_tol() reset_rtol() reset_atol(): Reset all model tolerances

revar(): Get model random effect variances and covariances

see(): Print model code to the console

smat(): Manipulate SIGMA matrices

simargs(): Access or clear arguments for calls to mrgsim()

soloc(): Return the location of the model shared object

solversettings: Optional inputs for lsoda

summary(<mrgmod>): Print summary of a mrgmod object

c(<tgrid>) c(<tgrids>) `+`(<tgrid>,<numeric>) `*`(<tgrid>,<numeric>) `+`(<tgrids>,<numeric>) `*`(<tgrids>,<numeric>): Operations with tgrid objects

tscale(): Re-scale time in the simulated output

update(<mrgmod>) update(<omegalist>) update(<sigmalist>) update(<parameter_list>): Update the model object

use_custom_tol() use_scalar_tol(): Set up a model object to use either scalar or custom tolerances

valid_data_set() valid_data_set.matrix(): Validate and prepare data sets for simulation

valid_idata_set(): Validate and prepare idata data sets for simulation

within(<mrgmod>): Update parameters, initials, and settings within a model object

zero_re(): Zero out random effects in a model object