`create.poped.database()`

now uses a better method of identifying the total number of parameters of each type (bpop, d, sigma, etc.) in a user defined model parameter function (the`ff_fun`

argument in`create.poped.database()`

) (#73).`create.poped.database()`

has a new option`reorder_parameter_vectors`

, which is turned off by default. When turned on, if you use named arguments in`bpop`

or`d`

then PopED will try to figure out the order of the parameters based on what is found in the`fg_fun`

. See the resulting`poped_db$parameters`

and make sure the order matches with`fg_fun`

.`start_parallel()`

has a new default for`num_cores`

, which is now one less than the number of cores available from`parallel::detectCores()`

.`model_prediction()`

and therefore`plot_model_prediction()`

allow for log-normal distributions when using the PI option. This makes sense if you expect the prediction interval of the model will be approximately log-normally distributed, which might often be the case in pop PK models. The new default is now`PI_ln_dist = TRUE`

.`poped_optim()`

now has an explicit argument allowing for the specification of Ds-optimal parameters of interest. The option is`ds_index`

.Minor bug fixes

Added the options

`allow_replicates=TRUE/FALSE`

,`allow_replicates_xt=TRUE/FALSE`

and`allow_replicates_a=TRUE/FALSE`

to`poped_optim`

. This allows the optimization algorithm to avoid replicates (or not) in the design components. Currently only works for discrete variable optimization. Future versions will also handle continuous optimization.Exported a function for the computation of the Bayesian Fisher information matrix for individual parameters of a population model based on Maximum A Posteriori (MAP) estimation of the empirical Bayes estimates (EBEs) in a population model. See

`?evaluate_fim_map`

for more details.Allowing for no covariates in the function that automatically builds a PopED parameter function from a model function (

`?build_sfg`

).Updates to documentation and package testing.

Minor bug fixes.

Added the ability to incorporate limit of quantification information into FIM calculations (both upper and lower limits). See the new vignette on the webpage https://andrewhooker.github.io/PopED/articles/handling_loq.html

Adding functionality to optimize groupsize and total size of the study. See

`?optimize_groupsize`

, ``?optimize_n_eff`

and`?optimize_n_rse`

. This is also implemented in`poped_optim`

through the`opt_inds=T`

argument.Updating Vignettes, including a new one about using other tools to use as simulators for design computations. See https://andrewhooker.github.io/PopED/articles/model_def_other_pkgs.html

Simplify RxODE syntax in the above vingette (#47, @mattfidler).

Added the ability to predict and plot model prediction intervals by computing the expected variance (using an FO approximation) and then computing a prediction interval based on an assumption of normality. See

`?model_prediciton`

and`?plot_model_prediction`

. The computation is faster but less accurate compared to using`DV=TRUE`

(and`groupsize_sim = 500`

) in the two functions.Named parameters are now passed to all calculations so that the FIM and RSE output is more readable with parameter names instead of default names.

Allow for parallel computation in

`plot_efficiency_of_windows`

(#50).Make parallelization work with mrgsolve on windows (#37, #45, #46, #51, @Vincent-AC).

Updated the function for automatic building of parameter model function (see

`build_sfg`

).Simplify derivative calculations (#34, @martin-gmx).

Allow for only simulating model_switch > 1 models.

Change the defult Ds calculation to be on log scale.

Updated the website at https://andrewhooker.github.io/PopED

Remove options for discontinued dplyr commands

`rbind_all`

and`rbind_list`

.Minor bug fixes in shrinkage calculations (#44, #39, @martin-gmx).

New and improved vignettes (#30, @giulialestini)!

Added power evaluation script to test the power of a design to identify a parameter different than an assumed value. The function also calculates the number of individuals needed in a design to have a specific power. See

`?evaluate_power`

for more information (#26, @martin-gmx).Added function to compute expected shrinkage of a design. See

`?shrinkage`

for more information.Updated and added new example scripts in

`system.file("examples", package="PopED")`

(). This includes an example describing how to handle covariate distributions in optimal design, an example on how to incorporate IOV, an example on how to handle shrinkage, an example with a full covariance matrix and an example with a prior FIM (#30, @giulialestini and @martin-gmx).Major overhaul in optimization methods used in

`poped_optim()`

so that generic optimization routines like`optim()`

can be easily used in optimizing PopED designs.Update speed of FIM calculations (#20, @martin-gmx).

Update RSE calculations so that prior FIM is handled correctly (#22, @martin-gmx).

Simplified code and removed duplicated code (#21, #24 and #32, @martin-gmx).

New ways of handling inverting matricies, should be faster and work better when the matricies are ill-conditioned. See

`?inv`

for more information (#19, @martin-gmx).Updated functionality of IOV calculations.

Updates to

`optim_ARS()`

for when to stop search.Extended functionality of

`plot_model_prediction()`

(#23, @martin-gmx).Bug fixing. See https://github.com/andrewhooker/PopED/commits/master for more information.

Exported the

`summary`

method for the results of`poped_optim`

in the PopED NAMESPACE, so that the method can actually be used! Just use`summary(output)`

.Fixed some old bugs that used

`return`

as a varible in functions, a la MATLAB.

Added a vignette to introduce PopED!

Improved optimization with

`poped_optim`

, plus all example scripts now running with`poped_optim`

.Update to more easily allow discrete optimization of xt and a variables. See the example scripts.

Added a summary method for the results of

`poped_optim`

. Just use`summary(output)`

.changed handling of seed numbers in optimizations.

more robust handling of non-population models

more natural handling of NA values in design vectors

NAMESPACE: removed ggplot2 from “Depends” and added to “Imports”

Added mean line to efficiency plots.

Update to computation and error handling for Laplace approximation to ED objective function.

Added more intuitive cost function input. See examples in

`?poped_optim`

Various small changes and bug fixes.

Added new optimization methods and tools, see

`?poped_optim()`

. This function incorporates the new optimization routines`optim_ARS()`

and`optim_LS`

which are optimized versions of previous optimization algorithms used in PopED. Both can be run with parallelization.`poped_optim()`

also incorporates the genetic algorithm from`GA::ga()`

, which can also be run with parallelization, and the “L-BFGS-B” method from`stats::optim()`

.`poped_optim()`

should be more efficient and faster than`poped_optimize()`

.Changed the default objective function to be the log of the determinant of the FIM.

`create.poped.database(ofv_calc_type=4)`

Various small changes and bug fixes.

Fixed

`plot_efficiency_of_windows()`

bug that had wrong headers on each subplot.Fixed bug in

`plot_model_prediction()`

that did not plot the optimized design, but instead the initial designReorganized the database created from

`create.poped.database()`

. The output from this function is now a list with 5 sub-lists: design, design_space, model, parameters and settings. Also removed duplicate entries in the database for easier manipulation. This will cause some back compatibility issues when referring to elements in a database.Added example 10 describing a PKPD design of hepatitis C virus (HCV) kinetics to the

`system.file("examples",package="PopED")`

directory of the PopED installation.

Updated model_prediction() to allow for creation of NONMEM datasets.

Useful for testing of optimized designs via PsN’s (http://psn.sf.net) SSE tool, for example.Two new functions create_design() and create_design_space() that allow for design and design space creation without the need for a model or parameter values.

Updated the create.poped.database() function to use create_design() and create_design_space()

Added examples for evaluation and optimization of a one-target quasi-steady-state target mediated drug disposition model (TMDD) to the system.file(“examples”,package=“PopED”) directory of the PopED installation.

Added a 2-compartment, oral absorption, multiple dose example to the system.file(“examples”, package=“PopED”) directory of the PopED installation.

Updated plot_efficiency_of_windows() to allow for the plotting of the RSE of each parameter on the y-axis.

Updated error handing for the Laplace approximation of the ED OFV.

Fixed bug when computing FIM with only one BSV term present in model (calculation gave an error).

Fixed a bug in plot_model_predictions where an error was returned if not all time values in the xt matrix were to be used for the design calculation (ni is different from size(xt,2), see ?create_poped_database).

Various small bug fixes.

Updated package author list

New functionality to compute the ED OFV using the Laplace approximation. This can be orders of magnitude faster than the standard MC integration approach. See ‘?ed_laplace_ofv’ and ‘?evaluate.e.ofv.fim’

Added a general function to compute the FIM and OFV(FIM) for all available methods in PopED. See ‘?calc_ofv_and_fim’.

Added a general optimization algorithm ‘RS_opt_gen()’ that works for both D-family and E-family optimization.

Added optimization of E-family designs to ‘poped_optimize()’.

Changed distribution tests for package building

Fixed bug where correlations between BSV (between subject variability) terms in the model gave an error when creating a PopED database

Fixed a bug where get_rse failed when a parameter had a value of 3.

- PopED has been translated to R from MATLAB and this is the initial release.