# NMsim 0.2.7

## New Features

* `prioritizePaths()` is a convenient function that allows the user to provide multiple candidate paths and automatically pick the first one available. This is convenient using `NMsim` because the Nonmem installation path often varies from system to system when sharing code between users, or even when as a user porting scripts between systems. For instance, this could be to accommodate running on a linux and a windows system, and sets the default to the first of the two available.

```
library(NMdata)
NMdataConf(path.nonmem = prioritizePaths(c(
  "/opt/NONMEM/nm75/run/nmfe75",
  "C:/nm75g64/run/nmfe75.bat"))
  )
```

## Bugfixes

* In some cases, `NMsim` needs to replace zeros by tiny numbers in Nonmem control streams and it uses 1E-30 for this. In some cases, that would be printed without scientific notation leading to too long number representations, and Nonmem will throw an error. This has been fixed so it now makes sure to print 1E-30 with scientific notation. Thank you Chengjun Jiang for reporting this (https://github.com/NMautoverse/NMsim/issues/40).

# NMsim 0.2.6

## New Features

* `NMsim_NWPRI` gains an argument, `add.diag`, to add a value to
  variance-covariance diagonal. This can be used in case the
  variance-covariance matrix has (typically numerically very small)
  negative values.

## Bugfixes
* `NMsim_NWPRI` would fail on models with `$OMEGA BLOCK(N) SAME`
  structures. This is often used for between-occasion
  variability. This has been fixed, even though only single omega
  parameters can be used with `SAME` at this point. Also, each SAME
  block must be written out, as the `SAME(N)` notation for repeating
  `SAME` blocks is still not supported by NMsim. Thanks to Brian
  Reilly for working on this.

* `NMsim_EBE` now works when no data set is supplied. This can be used
  to get individual `PRED` and `IPRED`, especially if these are
  evaluated differently in the estimation control stream (e.g. using
  M3, `PRED` gets a different interpretation at censoring).

* `name.sim` argument to `NMsim()` now supports strings ending in
  periods, e.g. `name.sim=string..` now works.

* `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.

## Other Improvements
* `NMsim_VarCov` has become faster, reducing the time spent setting up
  simulations by around 1/3, depending on the simulation problem.

# NMsim 0.2.5

## 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

## 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

## New features
* `NMaddSamples()` gains a `by` argument. This makes `NMaddSamples()`
  easy to use for generation of both nominal sampling schemes and
  recreation of observed sampling schemes.

* Streamlining of `NMsim_VarCov()` and
  `samplePars()`. `NMsim_VarCov()` now always calls `samplePars()`
  internally. `samplePars()` uses the `method` argument to switch
  between `mvrnorm`(multivariate normal distribution) and `simpar`
  (inverse Wishart distribution.

* `NMsim_VarCov()` gains `method.sample` argument which is passed as
  `method` to `samplePars()`. This means `NMsim_VarCov()` can be used
  to simulated with parameter uncertainty using either `mvrnorm` or
  `simpar`.

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
## 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
## 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 `ETA`s for use in
  future model simulations with the same synthetic population
  (`ETA`s). If the `.phi` file that stores the `ETA`s 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

## 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

* 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 `THETA`s. 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

## 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

## 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

## 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
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
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
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`](https://nmautoverse.github.io/NMsim/articles/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
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
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

## 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
## 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
## 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
## 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
## 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"`.
