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Changelog

Source: NEWS.md

NMsim 0.2.6

Bugfixes

  • name.sim=string.. accepted

  • dir.sims and dir.res relative paths starting with ../ now work. Example: dir.sims="../simtmp" would fail. Fixed.

  • sampleCovs would fail if input data set did not include an EVID column. Fixed.

NMsim 0.2.5

CRAN release: 2025-09-06

New features

  • Method to run “typical subject” simulations has been improved and the code has been simplified. This is controlled using the typical argument in NMsim(). The basic use of this argument is typical=FALSE (default, not a typical subject simulation) or typical=TRUE. If typical=TRUE, $OMEGA parameters are fixed at zero. If the model has OMEGA prior parameters $OMEGAP and $OMEGAPD, those are fixed to zero too. Instead of a logical (TRUE/FALSE), a character vector can now be supplied to specify what oarameter types to fix at zero. E.g. to also drop residual variability, use typical=c("omega","sigma") (remember to add the prior parameter types if needed).

  • A new NMsim() argument dir.sim.sub controls whether a subdirectory is created in dir.sims for Nonmem execution. When using NMsim() for estimation, it may be more convenient to use dir.sim.sub=FALSE to get all model executions in the same directory.

  • NMcreateDoses() has a new argument, N, allowing for creation of multiple subjects.

Bugfixes

NMsim_NWPRI() in combination with typical=TRUE would fail in some cases. This has been fixed with the new implementation of the typical argument.

Other improvements

NMsim() argument name.sim supports stings with parenthesis.

NMsim 0.2.4

CRAN release: 2025-07-16

New features

  • NMsim()’s support for handling and control of initial values (the inits argument) has been greatly improved. In recent versions a new method for reading, updating, and writing the parameter definitions in control streams has been implemented. This allows for modification of this information and for fixing/unfixing parameters. The downside is it comes with some limitations to control stream syntax. With 0.2.4 those limitations are getting rare. The original much simpler method is still available and provides a robust alternative for many simulation purposes and is kept as a fallback workaround.

  • NMsim()’s init argument supports ext and inits.tab formats. These interfaces to specifying parameter values greatly improves flexibility for programming, and for specifying multiple new parameter sets for series of model runs.

  • NMreadSim() gains the skip.missing argument. In case some model runs fail or haven’t finished, this allows NMreadSim() to read whatever it can and skip the ones missing.

  • Summary function included on NMsim simulation results. There is still room for improvement - try it out with summary() on results from NMsim() and NMreadSim().

Bugfixes

  • NMREP is a data column that NMsim() automatically adds to count subproblems when subproblem is used. This was not added when simulating PREDPP ($PRED) models and when not providing a simulaiton data set (VPC style). Thanks to Ahmed Abulfathi for reporting.

  • In some cases NMsim_NMWPRI() in combination with typical=TRUE would create wrong $PRIOR dimensions. Fixed.

Other improvements

  • NMaddSamples() only inserts MDV column if MDV is already present in data.

  • NMexec() uses the -maxlim=2 option when executing Nonmem. This is implemented on both the internal execution method and when PSN’s execute is used.

NMsim 0.2.3

CRAN release: 2025-05-20

New features

Messages have been implemented in samplePars() to summarize number of truncations in case mvrnorm results in negative variance parameters.

Bugfixes

NMsim_VarCov() sampling of OMEGA/SIGMA. Affects simulation with between-subject variability. Big thanks to Sanaya Shroff for reporting and fixing.

Other Improvements

  • Improved defaults for whether to suppress Nonmem messages (nmquiet argument). Particularly, when NMsim is not waiting on Nonmem runs, it will by default suppress those messages. Basically, NMsim will now by default only show Nonmem terminal messages if it is waiting, and only one Nonmem model is executed. Notice, that quiet=TRUE implies suppressing both NMsim and Nonmem messages and nmquiet=TRUE.

Other Changes

  • The deprecated addEVID2() is kept as a snapshot of when NMsamples() was introduced. The new by argument is not compatible with the default behavior of the deprecated addEVID2(). This is to ensure reproducibility of existing code on the user side.

NMsim 0.2.2

CRAN release: 2025-05-06

New features

  • sampleCovs() is a new function to conveniently sample subject-level covariates from an existing data set.

Bugfixes

  • There was a bug introduced in version 0.2.1 leading NMsim() to throw an error saving some data sets. The issue is fixed.

  • The default Nonmem $TABLE format is insufficient with NMsim’s way to create row counters when simulating 1e5 rows or more. NMsim is now by default using a format with a much higher resolution. Thanks to Sanaya Shroff for helping debugging this.

NMsim 0.2.1

CRAN release: 2025-04-24

New Features

  • NMaddSamples() replaces the addEVID2() function. addEVID2() will still work and redirects to NMaddSamples() for backward compatibility. Your existing work will still work but you will get a (suppressable) message about the name change. Especially due to work on optimal sampling with NMsim by Ahmed Abulfathi and myself, we need a more flexible interface for adding samples.

  • NMaddSamples() comes with a new argument DV. By default NMaddSamples() (used to be, addEVID2()) adds rows without DV values and with EVID=2 and MDV=1. If DV is supplied, NMaddSamples() will include that value in the DV column and by default use EVID=0 and MDV=0. An example where this is useful is when generating datasets for $DESIGN where DV=0 is often used.

  • samplePars() is a new function that replaces sampleParsSimpar(). samplePars() takes the method argument which can be used to switch between multivariate normal distribution method="mvrnorm" and using the simpar package to get use Inverse-Wishart distribution for $OMEGA and $SIGMA parameters. Notice, both methods are fully automated in NMsim - all you need to write is the path to a control stream and the number (nsims) of parameter sets wanted.

  • simPopEtas() by default does not overwrite an existing .phi file. simPopEtas() is used to generate sampled ETAs for use in future model simulations with the same synthetic population (ETAs). If the .phi file that stores the ETAs gets overwritten using a new seed, it will affect simulations using that .phi file. Overwriting the .phi file with different seeds should therefore be avoided, and this new behavior of protecting the generated .phi files reduces that risk.

Bugfixes

  • NMsim_VarCov() would not include $OMEGA and $SIGMA blocks which lead to errors in Nonmem. This bug was likely introduced in NMsim 0.1.6 and has now been fixed.

  • Version 0.2.0 gave some warnings about non-existing columns. The warnings are benign and can be safely ignored. They are avoided in Version 0.2.1.

  • Updating initial values in models using SAME argument on random effects (e.g. in between-occasion variability) would fail if the effects were fixed. Resolved. Thanks to Sergio Iadevaia for reporting this.

  • Data sets with commas in character columns would make NMsim fail. Support for such data sets had not been carried over with the new data handling approach implemented in version 0.2.0. This has now been resolved.

  • NMcreateDoses() had a bug in the addl.lastonly feature which would throw errors when TIME was longer than two. addl.lastonly=TRUE is default and means that if the length of the ADDL argument is one, it will be applied to the last dose only. This is for the common case where initial doses (say a load or an initial escalation phase) are followed by a maintenance regimen. The bug has been resolved.

  • NMaddSamples() would fail if using the TAPD argument when TAPD was already available as a column in data. Fixed.

Other Improvements

  • The configuration of job submission is simplified when jobs are run in parallel with single-core processing.

  • Checks have been included in summarizeCovs() for whether NA’s are produced. summarizeCovs() is used to summarize simulated covariate effects (typically for forest plots). Especially, if for some reason the reference exposure is zero (likely an error by the modeler), summarizeCovs() would throw an error. Now summarizeCovs() will try to identify this, give useful messages back, and report the estimates.

  • Consistent ordering of columns in simulation results independently of col.id and col.time.

NMsim 0.2.0

CRAN release: 2025-03-13

New Features

  • A greatly improved handling of data files has been implemented. This improves speed, reduced memory and disk usage, and adds features. It is fully backward compatible with the user interface of earlier versions.
    • Data sets are only saved once for each model.
    • Output tables are written and read more efficently. This is obtained by a combination of default TABLE options and an efficient method to read the tables if those options have not been changed by the user. If the user does change them, a more robust but slower and more memory intensive method is used.
    • Input and output data are now by default merged using a row identifier inserted by NMsim. This makes NMsim robust to Nonmem code that does not simulate all rows in input data.
    • Subproblems are automatically counted in the NMREP column in output. Force the inclusion of this column using the nmrep argument if needed even if not using subproblems.
  • Variables from input data to be included in results can be specified using the new carry.out argument. The default behavior by NMsim() is to include all variables from input data on the result. However, if the data set contains many rows or columns, this can be memory demanding. Now you can minimize memory use and maximize speed by limiting the variables in both input and output. For example
simres <- NMsim(file.mod,data,
                table.vars=c("PRED","IPRED","Y"),
                carry.out=c("ID","TIME",`EVID`)
                )

simres will in this case contain only PRED, IPRED, and Y from the output table, and only ID, TIME, and EVID from the input data.frame (data). NMsim() also takes a new argument table.format which can be used to adjust the table format. But normally, that is not be necessary. There is no reason to list ID or any other column from the input data in table.vars since they can be carried over directly fro the input data, avoiding potential loss of accuracy in writing and reading to and from text files. You do not need to worry about merging input and output data correctly - NMsim() handles that internally using its own row identifier.

  • Handling of Nonmem data filters. In case a sim is run without an simulation input data set, NMsim() by default reuses the estimation data set and the IGNORE and ACCEPT statements in the estimation control stream. This is very useful for visual predictive check (VPC) simulations. However, the aim may be to run the simulation on the same data set with differnt inclusions. A common example of the is if the estimation was run without samples below the quantification limit (M1), but the simulation also be performed on those samples. This can now be done by passing new filters to NMsim(). In fact, this can even be done by first reading the filters from the control stream, then easily editing them, before passing them to NMsim().

Other changes

  • NMsim()’s argument modify.model has been renamed to modify. This is to align argument names with other arguments available for model modification, namely inits, sizes, and filters.

  • NMcreateDoses() requires AMT to be provided.

  • addEVID2() only requires CMT argument when the column of the same name is present in data. Not all models require CMT and this change allows for building such data sets with NMcreateDoses() and addEVID2().

NMsim 0.1.6

CRAN release: 2025-02-05

  • A major improvement is implemented on NMSim_NWPRI(), the simulation method that leverages the Nonmem NWPRI subroutine to simulate models with parameter uncertainty. This method was first included in NMsim 0.1.3 but - as was clearly declared in that version - it could only be trusted for simulation of THETAs. After further development in both NMsim and NMdata to support this as well as bugfixes in the new Nonmem 7.6.0, NMsim provides full support for simulation with parameter uncertainty using the inverse Wishart distribution through this simple interface.

  • A new arguments inits is introduced to manually specify parameter (initial) values. This is the values that go into $THETA, $OMEGA and SIGMA sections of the control stream. To simulate with the final estimated values (stored in a .ext file), simply add inits=list("theta(1)"=list(init=1.2)). For simulation, only the parameter values (init) may be of interest, but if you are using NMsim for estimation too, bounds and whether parameters are fixed can now also controlled. BLOCK structures in $OMEGA and SIGMA can currently not be changed (say, correlation of two random effects cannot be introduced or removed).

  • The $SIZES can now easily be controlled using the simple sizes argument in NMsim(). It leverages a new function NMupdateSizes() which can be used to edit $SIZES independently of the NMsim() function. In NMsim() just add the argument like sizes=list(PD=100) which will update or add $SIZES PD=100 as needed. See documentation for more details.

  • Nonmem execution

  • Improved monitoring of Nonmem jobs. In NMsim 0.1.5, NMsim() would not always catch and properly handled failed runs. On Linux, this is much better handled now. On Windows, failures still may not be caught properly - more work to be don on Windows to align with the approach on Linux.

  • A new post.fun argument has been introduced in NMexec() to run additional code once Nonmem has finished. This can be used to automatically initiate creation of goodness of fit plots, simulations or any full workflows run using Rscript after estimation of models.

Bugfixes

  • overwrite() is a helper function intended to use in NMsim()’s modify.model argument. It would not work correctly for strings containing some special, at least. Fixed.

  • NMsim_NWPRI would not always paste the full variance-covariance matrix for theta estimates into $THETAPV which would make NONMEM fail. Fixed.

NMsim 0.1.5

CRAN release: 2024-11-14

New features

  • NMreadSim() has a new argument called rm.tmp which is used to remove the intermediate NONMEM files if they are successfully read. Remember, NMreadSim() creates a compressed results data set which will be read by NMreadSim() in future function calls, so unless debugging is needed on the simulation control streams and files returned by NONMEM running the simulations, it may be better to delete the intermediate files altogether and save the disk space.

  • expandCovs() has a new argument reduce.ref which defaults to TRUE meaning that by default there will be only one reference combination. If FALSE expandCovs() will return one reference for each covariate. The forest plot can be evaluated with just one reference simulation.

  • New function summarizeCovs() introduced to summarize simulation results for forest plots. This function is closely related to expandCovs()

  • NMsim() no longer requires NONMEM to be available if reuse.results=TRUE and NONMEM does not need to be run.

Bugfixes

  • NMsim 0.1.4 would not submit jobs to cluster when number of cores was nc=1. Fixed. Workaround in 0.1.4, use nc=2.

  • NMsim 0.1.4 erroneously concluded jobs had failed when sent to the cluster if there was no existing queue. This is due to the exit status of qsub in such cases. This has been fixed. As a workaround in 0.1.4, just run your sim again once the queue has been initiated by the first NMsim called.

  • When NONMEM failed in 0.1.4, NMsim might not return debugging info. Fixed.

NMsim 0.1.4

CRAN release: 2024-11-02

New features

  • sampleParsSimpar() is a new function that automates sampling of parameter values from an estimated variance-variance matrix in a successful $COVARIANCE step using the simpar R package from Metrum Research Group. simpar is currently not on CRAN, so the user must install it from MPN or github to make use of sampleParsSimpar(). The sampled parameter values can be fed directly to NMsim using the NMsim_VarCov method making it very easy to simulate with parameter uncertainty based on simpar. I want to thank Sanaya Shroff for her outstanding work on this functionality and for her exciting work summarizing the available methods for simulation with parameter uncertainty which she will be sharing at ACoP 2024. Also a big thanks to Eric Anderson for helping out with adjusting the github workflows to pull simpar from MPN.

  • expandCovs() is a new function that puts together data sets for univariately varying covariates while keeping other at reference values. The function can derive both reference values and covariate values to simulate at by using i.e. median() and quantile().

  • NMsim()

    • Results are now equipped with three columns distinguishing simulated models. This separation of information makes it easier to summarize simulation results within/across models and/or within/across simulation of models.
      • model: The run name derived from file.mod.
      • name.sim: The same as provided in the name.sim argument.
      • model.sim The name of the generated model. In the simple case, this is model and name.sim combined. But in many cases, multiple models are being generated for each simulated control stream.
    • No longer requires a .ext file if updating parameter values using PSN’s update_inits. It is still recommended to keep the .ext file since it provides higher accuracy than the .lst file.

  • NMexec()

    • When submitting all updated models, NMexec() will now by default try to detect if a model is already running before submitting it.

    • Provides a summary of models to be submitted before starting to do so.

  • NMcreateDoses()

    • ADDL and II are now also separate arguments providing a simpler interface than the addl argument. The addl argument provides the advantage of being able to specify the two columns together in one data.frame, possibly including covariates.

    • add.lastonly is a new argument. If TRUE (default) and ADDL and II are of length 1, they are only applied to the last event in a dosing regimen.

    • col.id argument to specify name of subject id column or to omit altogether using col.id=NA.

    • Now checking that TIME is covering the length of all other arguments. In contrast to other arguments, it does not make much sense to try to extrapolate the TIME argument.

  • addEVID2() now has two arguments, TIME and TAPD which allow for specification of time since first dose and time after each dose at which to insert simulation records. The two can even be combined. TIME replaces the now deprecated time.sim argument, and TAPD is new.

Bugfixes

  • A bug most likely affecting most Windows users for execution of Nonmem has been fixed. If on Windows, you should upgrade to NMsim 0.1.4. Thank you to Boris Grinshpun for reporting this!

  • When using method.execute="nmsim" there was an issue with parallellization. This was not a major problem in most simulation applications, but it should now be fixed.

  • NMsim()

    • When not providing a simulation data set - typically a simulation for a VPC - NMsim() would fail with messages like
Error in `:=`((col.sim), ..name.sim) :
  Check that is.data.table(DT) == TRUE. Otherwise, := and `:=`(...) are defined for use in j, once only and in particular ways. See help(":=").

The issue has been fixed. If using NMsim 0.1.3 or earlier, the workaround is to do NMdataConf(as.fun="data.table"). Then after having the simulation results as a data.table, convert it with as.data.frame() or as preferred.

Notice, NMdataConf() affects the succeeding NMsim() calls but also other NMdata and NMsim function calls. When the VPC simulation has run, you may not want to continue recieving data.tables, you should reset the default value for as.fun: NMdataConf(as.fun=NULL) which will turn it back to returning data.frames by default. If you prefer tibbles, you can do NMdataConf(as.fun=tibble::as_tibble). Generally, if you prefer to work with something that is not data.frames (data.table and tibble the most common alternatives), it is recommended to use NMdataConf() to customize your default.

  • NMexec()
    • NMexec would fail running control streams named starting in numerals (like 1.mod) when sge=TRUE. This is due to the way sge job names are generated by NMexec(). Fixed by prepending “NMsim_” in these cases.
  • NMcreateDoses
    • Would in some cases create too many replicates if there were covariates on multiple arguments. Fixed.

NMsim 0.1.3

CRAN release: 2024-08-26

New features

  • NMsim()

    • New simulation method NMsim_NWPRI() to simulate with parameter uncertainty. This automates simulation with parameter uncertainty using Nonmem’s NWPRI subroutine for models with a successful covariance step. For now this method only works for THETA since we have found that the parameter distributions sampled for OMEGA and SIGMA do not always match the model estimates and therefore cannot be trusted. To ensure that only THETA are sampled and simulated, this method should only be run using the typical=TRUE argument. This method is much faster than the existing methods in NMsim for simulation with parameter uncertainty (NMsim_VarCov()). This method depends on NMdata version 0.1.7 or greater. Big thanks to Brian Reilly for his excellent work on this important contribution.

    • The add() function to be used in NMsim()’s modify.model argument now supports prepending and appending of text lines to control stream sections. The default is still to append and add("text",.pos="top") will now prepend “text”.

  • NMexec()

    • A “cleaning” feature has been added to NMexec(), removing some of the temporary files generated by Nonmem after ended execution. The interface is inspired by PSN’s clean argument supporting values 0 (no cleaning), 1-4 (quite some cleaning - so far no difference betwen these values), and 5 for complete deletion of the temporary directory. When using method.execute="PSN" NMsim calls PSN’s execute passing on the clean value.

    • Default number of cores to be used by NMexec() can be controlled using NMdataConf(nc=N) where N is the desired default. Notice, NMsim() will not use this default. This is because parallellization is not as commonly used in simulation as in estimation.

    • A useful backup feature has been added. Before execution, any existing results files related to a model are by default moved to a backup folder. The backup files are not

  • NMsimTestConf() - Function to summarize and test configuration. This is used internally and provides important debugging information.

  • readParsWide() - A function to read wide-format parameter tables - is now exported. This is useful when simulating with parameter values that have been sampled outside Nonmem, e.g. using the simpar package.

Bugfixes

  • NMexec() would fail on linux when run on models with multiple $TABLE statements. Fixed.

  • NMsim’s internal method to update parameter initial values had an issue running on models with $OMEGA block structures. Fixed.

  • NMreadSim() would fail if working directory had been changed. Fixed.

Other changes

  • addEVID2 will no longer add a DV=NA column if DV is not in the input data set.

NMsim 0.1.2

CRAN release: 2024-07-15

No changes since 0.1.1 except for disabling a test that was failing on some systems due to technical reasons.

NMsim 0.1.1

CRAN release: 2024-07-03

While no critical and generally only few bugs have been found in NMsim 0.1.0, NMsim 0.2.0 includes several improvements and upgrades. The interface feels smoother too. I want to thank Ron Keizer for feedback and advice.

New features

  • NMsim() has a much reduced and improved messaging to the user. If more than one model or data set is supplied or generated, progress trackers will be shown while starting Nonmem, while waiting for Nonmem to finish, and while collecting the simulation results.

  • The messages include information about where intermediate files and final results files are stored.

  • NMexec() has improved support for estimation. method.execute="nmsim" and method.execute="psn" both work on linux and windows, even though less thoroughly tested on windows. Thank you to Boris Grinshpun for testing.

  • Names of files containing final results from NMsim() have been renamed to be more intuitive. The previous _paths.rds will now be called _MetaData.rds. The results, once read and compressed, will be in a file called _ResultsData.fst. Notice, both these files are required to fully recover simulation results. Thanks to Brian Reilly for discussions on this and many other design aspects.

  • It is now possible to provide specific parameters (THETA, OMEGA and SIGMA) for Nonmem simulation. NMsim() table for simulations. See argument file.ext and NMsim_VarCov’s argument ext.

  • New arguments to control seeds. NMsim can either use R’s set.seed before generating the seeds for Nonmem. Detailed control of the seeds, including how many to include and the distribution of the random sources in Nonmem, can be controlled using the seed.nm argument. This way, the user can add random processes to the estimated control stream. The actual Nonmem seed values can also be provided.

  • method.sim=NMsim_typical() has been replaced by argument typical=TRUE. This means typical subject simulations can now be combined with other simulations methods like NMsim_VarCov.

  • NMsim() now adds a column called sim which carries the name of the simulation defined by the name.sim argument.

  • Several checks for existence and consistency of files are implemented.

  • The native Nonmem execution method now also works for estimation.

  • pnm files are now saved with the model for transparency.

Bugfixes

  • Running rbind on results from NMsim would throw errors. Thanks to Simone Cassani for reporting this. Fixed.

  • Using other file name extensions than .mod on input control streams in combination with NMdataConf(file.mod) would make NMsim fail. Thanks to Brian Reilly for reporting. Fixed.

Other changes

  • NMsim_known() renamed to NMsim_EBE().

  • Generated control streams have been stripped off of the “NMsim_” prefix. These files are located in NMsim generated folders so the prefix was uninformative.

  • In case of multi-threaded (cluster) execution and something went wrong NMexec() used to write some output files from Nonmem in the current working directory. All these are now being written to the model execution directory for clarity and tidyness.

NMsim 0.1.0

CRAN release: 2024-02-22

For the first time NMsim works on Windows. There may still be some limitations but initial testing looks very promising. Make sure to set path.nonmem. See the configuration vignette on the website: NMsim-config.html

0.1.0 is also an important upgrade that solidifies the way NMsim reads results from simulations. In addition to important bug fixes, it allows for NMsim to wait on Nonmem to complete simulations - even when they are run on a cluster. This means even large simulations with NMsim can be integrated in scripts.

New features

  • Works on Windows - at least most features do.

  • NMsim() and NMreadSim() now have wait arguments which controls if they will wait for Nonmem to finish simulating. This will also work if jobs were sent to the cluster.

  • NMsim() respects the reuse.results argument. If TRUE it will use results file on the file system. This can be used in stead of putting NMsim() calls inside an if-statement to disable the simulation but read results on file.

  • NMsim() looks for a couple of features of the provided control streams that are known to be able to cause issues. Warnings will be issued if these are found.

  • addEVID2 has a new argument, EVID to specify what value the EVID column should have. It can be useful sometimes to use EVID=0 for simulation records.

Bugfixes

  • In some cases NMreadSim() would not get the path right to the simulation results leading to failures in reading simulation results. Fixed.

Other changes

  • Functions NMreadExt and NMreadPhi have been removed from NMsim. They live and are being maintained in the NMdata package. In NMsim, were deprecated and unmaintained functions.

NMsim 0.0.10

CRAN release: 2024-02-08

NMsim 0.0.9 had an unfortunate bug in NMreadSim() which has been fixed. That bugfix is difference between 0.0.9 and 0.0.10.

NMsim 0.0.9

CRAN release: 2024-02-07

NMsim 0.0.9 is almost identical to 0.0.8 but ensures compatibility with older R versions.

Bugfixes

  • In some cases NMreadSim would not be able to read and combine results from models that returned different data variables. Fixed.

NMsim 0.0.8

CRAN release: 2024-02-07

New features

  • NMsim 0.0.1 would generate an rds file with paths to simulation files and results for each model+data set simulated. This has been changed to now only generate one table per model. This makes it simpler to read simulation results in some cases.

  • NMreadSim should now be the best and only way for the user to read NMsim simulation results. It interprets rds files (which are the ones intended for reading), fst files, tables of NMsim runs, and NMsim results. This makes it less confusing what can be processed by NMreadSim and also it sometimes easier to generalize code reading simulation results. Also, NMsim now always reads results using NMreadSim. This has the advantage that an fst file will always be produced if NMsim waits to read the results.

  • NMreadSim has a new argument, check.time by default disabling checking whether a collected fst file is newer than the results files generated by NMsim. Normally, it’s a good thing to check this but some ways of sharing file files may not retain file modification times needed to check for this. NMsim will delete the fst files if it finds any so normally it should not be a problem to skip this check.

  • modify.model is the argument to use to modify the control stream after NMsim is done preparing the simulation. A couple of helper functions are available making it really easy to add contents (very commonly used) or modify contents.

  • NMsim now tries to reuse stored results if reuse.results=TRUE. It does so in a simple way - if they exist, they will be attempted read - so be careful to rerun simulations without this option if you change any arguments.

  • NMsim will by default add a DV column with NA values if DV is not in input data. Nonmem most often needs that column, and it is uninformative for simulations. Disable this feature by using auto.dv=FALSE.

  • The transform option has been integrated into the table of simulations created by NMsim(). This means even if the results are not read by NMsim (because the simulation is not executed or it is submitted to a cluster), the transformation will still be applied by NMreadSim() later.

  • NMsim()'s dir.sims and dir.res arguments can be controlled using NMdata::NMdataConf(). Often these two arguments are used all the time, so it’s convenient to be able to configure those once and for all in a script.

Bugfixes

  • NMreadSim was only able to read results if the current working directory was the same as when NMsim was executed. Now fixed.

  • In some cases NMsim would fail on models with multiple output tables when the table.vars argument was not used. Fixed.

  • NMsim’s sim.dir.from.scratch argument was not respected due to a simple bug, leading to dir.sims growing each time a simulation was rerun.

  • In case simulation data is a list of data sets NMsim would not order columns when order.columns was TRUE. Now fixed.

  • In case of lists of data sets, and the list element (data set) names included spaces, NMsim() would throw and error. Spaces in data set names are now replaced with under scores (“_“) to avoid that. It will often happen when data sets are split into lists using data.table::split.data.table() - which is an excellent way to do this, by the way.

  • Function simPopEtas() was not exported, so only available as NMsim:::simPopEtas(). Fixed.

NMsim 0.0.7

CRAN release: 2024-01-08

New features

  • Function simPopEtas() to generate a population from a model. The population can be saved as a phi file to be reused in subsequent simulations. The function is by mistake not exported in 0.0.7 so for now you must use NMsim:::simPopEtas() to use it.

  • Function NMreadSim() provides a very simple interface to reading simulation results. Especailly in cases where the simulation is being parallelized or otherwise spawns multiple Nonmem jobs, this is a useful feature.

  • A list of simulation data sets will now be simulated with separate Nonmem runs. This is an efficient way to parellelize large simulation runs.

NMsim 0.0.6

CRAN release: 2023-11-28

New features

  • Support for parallelization of simulations added when using PSN. It used to be possible to run multiple simulations simultaneously in separate threads. Now single simulation runs can be parallelized on sge type clusters (using qsub). See arguments sge and nc.

Bugfixes

  • A simple mistake would create problems in genPhiFile() when having more than 10 ETAs in a model. Now fixed.

NMsim 0.0.5

CRAN release: 2023-11-11

New features

  • Full support for models estimated with SAEM. Especially, simulation of “known” subjects, i.e. re-using emperical Bayes estimates, is slightly different with these models.

  • Experimental support for windows with PsN. dir.psn argument has to point to a directory where executables execute and update_inits are found. Thanks to Sjoerd Koopman for debugging and testing this. Hopefully in future versions, PsN will not be needed on Windows (like it is not needed on Linux).

  • The simulation method called NMsim_known now accepts other .phi files to use than the .phi file generated by the estimation run. This is useful if one wants to reuse subjects generated in a previous simulation.

Other/minor improvements

  • NMexec now also copies out the shk (shrinkage estimates) file after a run. The files that will by default be copied and reported next to the control streams are now xml, ext, cov, cor, coi, phi, shk - in addition to output table files and the archived input data of course.

NMsim 0.0.2

CRAN release: 2023-09-14

New features

  • NMsim supports type.sim="typical" which means all OMEGAS will be fixed to zero. This requires the ext file to be present.

  • Experimental support for simulation of estimated subjects using type.sim="known".