A new release of Pumas is now available in the JuliaProRegistry. You can upgrade your current installation of Pumas by simply running ] up Pumas
at the command prompt in JuliaPro.
The new release is a beta release, and this means that we have a so-called feature freeze. This means that all breaking changes and all feature additions have been made for what will become Pumas v1.0. This also means that we have included some final breaking changes, so please consult the release notes below if code that used to work no longer works.
Release Notes for Pumas v0.14.0
Features and improvements
- Add VPC for continuous outcomes
- Add Time-to-event distribution type to allow single and repeated time to event analysis
- Add support for continuous-time Markov models using the functional interface
- Change ordering of internal operations in variance-covarinace matrix,
vcov(::FittedPumasModel)
, calculations. - Check that all elements of the
@param
block are present in the input parameterNamedTuple
infit
,simobs
andsolve
forPopulation
s - Use automatic differentiation for
NaivePooled
andFO
- Introduce a check for gradient elements that are exactly zero as these indicate bad starting values or problems with identifiability
- Add a method to
cond
so it can be called on aFittedPumasModel
to get the condition number of the variance-covariance matrix - Support
evid=3
andevid=4
inread_pumas
- Add explicit check that all required variables are present in
@pre
when using analytical solutions - Add likelihood ratio test (
lrtest
andpvalue
) - Allow mixed number of doses in a population in NCA analysis
- Add parallelization in
simobs
when simulating for several parameter inputs at once - Add convenience methods to
predict
to predict from aFittedPumasModel
onto a new dataset - Add
probstable
that returns the probabilities of discrete observed outcomes. Can be converted to a DataFrame. - Add
paralellization
to simulated diagnostic functions - Improve DosageRegimen docstring
- Improve error messages in NCA
- Introduce
icoef
to extract subject-specific variables. Can be converted to a DataFrame. - Make bootstrap output a FittedPumasModelInference struct
- Add
liftunits2header
as post-processing ofNCAReport
Deprecations
- Unexport
ConstDomain
usage in@param
. Useconstantcoef
infit
instead. - Use
η=0
for population predictions andη=η*
for individual predictions - Rename keywords
obs
anddvs
toobservations
,cvs
tocovariates
,evs
toevents
, andtimes
totime
Bugfixes
- Fix behavior in mixed (analytical and numerical) models due to changes in RecursiveArrayTools
- Fix order of rows in
NCAReport
to be sorted by (id
,group
) - Superposition fixes
- Keep
id
when creating aSubject
from aSimulatedObservations
- Require that input covariates are
NamedTuple
s in theSubject
to avoid a common mistake of omitting a comma (an assignment(a=1)
vs aNamedTuple
(a=1,)
) - Correctly pass the option to show progress information to internal functions when doing Bayesian inference.
- Fix bug in handling of multiple dosage regimens in models with analytical solutions
- Fix bug in
ϵshrinkage
when subjects don’t have the same number of observations - Fix bug in
bic
when there are multiple dependent variables and dependent variables with missing values - Fix various bugs in the DataFrame constructor for
Subject
. This includes cases with time-varying covariates, no observations, and no events. - Allow for placebo subjects (no events) in analytical models
- Fix issues of proper extraction of dependent variables when constructing a
Subject
from aSimulatedSubject
- Fix alignment of output in printing
Vector{<:FittedPumasModels}
- Fix bug in
Central1Peripheral1Metab1
Compatability
- RecursiveArrayTools versions 2.x are now supported
- DataInterpolations version 3.0 is now supported
- Optim versions 0.21, 0.22 now supported
- DataFrames 0.21. is now supported
- CSV version 0.7 now supported
- MCMCChains v4.0 now supported
- We now require Roots (v1.0)
- Remove GLM dependency