getMinAICFit() returns the fit with the lowest AIC,
listModelsTested() builds a
Description/AIC/dAIC table ready
for pander::pander(), and isBoundaryFit()
reports whether a fit has a parameter at its boundary. By default both
selection helpers exclude boundary fits. See the new “reporting helpers”
article.Fix bootstrapFit(stratVar=), which did not actually
resample. The stratified branch called
sample(list(uids), ...), and since list(uids)
has length one every draw returned the whole vector of subject ids, so
the bootstrap datasets did not depend on the seed (#99). Three further
problems in the same code are fixed with it: the new subject ids
restarted at 1 in each stratum, so subjects from different strata were
merged under a shared id; the sample was split across strata by the
number of observations rather than the number of
subjects, over-weighting strata whose subjects have more
records; and rounding each stratum up could return more subjects than
nSampIndiv asked for.
A stratified bootstrap now always draws whole subjects. When
stratVar changed within a subject, that subject’s records
were split between strata and resampled as separate (partial) subjects;
each subject is now stratified by its first value, with a
warning.
nlmixr2extra:::sampling() now resolves its
uid_colname default before using it. Called without one it
sampled ncol(data) subjects instead of the number of
subjects in the data. It also accepts a tibble, which previously
produced a one column tibble where a vector of subject ids was
expected.
Fix covarSearchAuto() crashing with “wrong arguments
for subsetting an environment” when a covariate is selected; the best
model is now re-fit to recover its fit object. Also corrected the
forward inclusion test, which had an inverted sign so improving
covariates were never selected (#103)
bootstrapFit() now works for models with a single
estimated population parameter, a single random effect, or no random
effects at all. Previously the bootstrap summary collapsed 1-row / 1x1
quantile arrays to vectors (and could not summarize a NULL
omega), causing bootstrapFit() to error with
dim(X) must have a positive length,
incorrect number of dimensions, or
'data' must be of a vector type, was 'NULL'. Printing the
bootstrap summary of a model with no random effects no longer errors
either.
optimUnisampling() now keeps N and
floorT when it retries internally. Before, the recursive
call reset them to the defaults, so asking for a sample size other than
1000, or for un-floored values, could silently return 1000 integer
samples instead (#97)
The bundled theoFitOde fit was regenerated and can
now be read without the qs2 package. Its
origData and parHistData had been serialized
with qs2, so without that package installed those slots
could not be decoded and fit$dataMergeInner() – and
anything built on it, such as the nlmixr2rpt figures –
failed.
Add focei/foce linearization
Add formula interface
Add vignettes on linearization, formula interface, log-likelihood profiling and preconditioning.
profile() method for likelihood profiling (Issue
#1)bootstrapFit() fixes se option (Issue
#66)bootstrapFit() now will be more careful handling
NA values so they do not completely affect results (Issue
#59)
bootstrapFit() will now only take the correlation of
the non-zero diagonals (Issue #59).
New method for knit_print() will generate model
equations for LaTeX reporting automatically.
Tests are now skipped if they contain linear compartment models that need gradients when the gradients are not compiled (as in the case of intel c++).
Use assignInMyNamespace() instead of using the
global assignment operator for the horseshoe prior
Be specific in version requirements (as requested by CRAN checks)
Move the theoFitOde.rda data build to
devtools::document() to reduce CRAN build time (could add
more standard models like warfarin for package developers which takes
way too much time for CRAN)
Fix cli issues with the new cli 3.4+
release that will allow bootstrapping to run again (before
cli would error, this fixes the donttest
issues on CRAN).
Fixed step-wise covariate selection to work a bit better with the updated UI, thanks to Vishal Sarsani
Added lasso covariate selection (thanks to Vishal Sarsani)
Added horseshoe prior covarite selecion (thanks to Vishal Sarsani)
Added a NEWS.md file to track changes to the
package.