poso_simu_pop() in v1.2.5 introduced several issues and have been revertedposo_replace_et() enables updating a model with events from a new rxode2 event table, while accounting for and interpolating any covariates or inter-occasion variabilityposo_time_cmin(), poso_dose_conc(), poso_dose_auc() and poso_inter_cmin().poso_simu_pop() provides an rxode2 model using the simulated ETA and the input dataset,
with interpolation of covariates, to make plotting easiervignette("route_of_administration") shows how to select a route of administration for optimal dosingvignette("population_models") describes the structure of prior population models written as model functions which can be parsed by rxode2 and used by posologyrvignette("posologyr_user_defined_models") is renamed vignette("classic_posologyr_models")rxode2 model functionsposo_estim_map(), poso_estim_sir() and poso_simu_pop() failed for models featuring a single parameter with IIV.poso_* functions. Once the model has been parsed by rxode2() with this package the model$posologyr gives the list needed for poso_* functionsposo_dose_conc(), poso_dose_auc() and poso_inter_cmin() where the returned estimate of the target value to be optimized against was always equal to zero.poso_time_cmin(), poso_dose_conc(), and poso_dose_auc() now explicitly states the consequences of setting tdm to TRUE: which parameters are required, which parameters are ignored, and which parameters behave differently.poso_time_cmin(), poso_dose_conc(), and poso_dose_auc() now return a warning if any of the input parameters are ignored.poso_dose_auc()posologyr() (as requested by CRAN)parent.frame() (as requested by CRAN)poso_estim_map(), poso_estim_sir() and poso_estim_mcmc() can now estimate individual PK profiles for multiple endpoints models (eg. PK-PD, parent-metabolite, blood-CSF...), using a different residual error model for each endpoint.poso_time_cmin(), poso_dose_conc(), poso_dose_auc() and poso_inter_cmin() now allow you to select the end point of interest for which you want to optimize, provided it is defined in the model.vignette("a_priori_dosing") illustrates a priori dose selectionvignette("a_posteriori_dosing") illustrates a posteriori dose selection, using TDM datavignette("auc_based_dosing") shows how to select an optimal dose for a given target AUC using data from TDMvignette("multiple_endpoints") introduces the new multiple endpoints featureposo_time_cmin() can now estimate time needed to reach a selected trough
concentration (Cmin) using the data from TDM directlyposo_dose_conc() can now estimate an optimal dose to reach a target
concentration following the events from TDMposo_dose_auc() can now estimate an optimal dose to reach a target auc
following the events from TDMposologyr() is now an internal function, all exported functions take
patient data and a prior model as input parametersposo_estim_map() provides an rxode2 model using MAP-EBE and the input dataset,
with interpolation of covariates, to make plotting easierposologyr() functionposo_time_cmin(), poso_dose_auc(), poso_dose_conc(), and
poso_inter_cmin() no longer fail for models with IOVposo_estim_sir() estimates the posterior distribution of individual
parameters by Sequential Importance Resampling (SIR). It is roughly 25 times
faster than poso_estim_mcmc() for 1000 samples.poso_estim_map() allows the estimation of the individual parameters by
adaptive MAP forecasting (cf. doi: 10.1007/s11095-020-02908-7) with
adapt=TRUE.poso_simu_pop(), poso_estim_map(), and poso_estim_sir() now support
models with both inter-individual (IIV) and inter-occasion variability (IOV).MASS:mvrnorm is replaced by mvtnorm::rmvnorm for multivariate normal
distributions.poso_estim_map() now uses method="L-BFGS-B" in optim for better convergence
of the algorithm.poso_inter_cmin() now uses method="L-BFGS-B" in optim for better convergence
of the algorithm.poso_dose_conc() is the new name of poso_dose_ctime().poso_time_cmin(), poso_dose_auc(),
poso_dose_conc(), and poso_inter_cmin() now work with prior and posterior
distributions of ETA, and not only with point estimates (such as the MAP).nocb parameter is added to posologyr(). The interpolation method for
time-varying covariates can be either last observation carried forward (locf,
the RxODE default), or next observation carried backward (nocb, the NONMEM
default).vignette("uncertainty_estimates") is removed.poso_time_cmin(), poso_dose_ctime(), and poso_dose_auc() now work for
multiple dose regimen.poso_inter_cmin() allows the optimization of the inter-dose interval for
multiple dose regimen.vignette("case_study_vancomycin") illustrates AUC-based optimal dosing,
multiple dose regimen, and continuous intravenous infusion.First public release.