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.
Release Notes for Pumas v0.11.0
LaplaceIapproximation is now available when fitting models with multiple dependent variables
coeftablefunction to produce a
DataFrameof coefficients from a
vcovfunction no longer throws an exception when the computation fails. Instead, it produces a warning and returns a failed
- Improvements to the compilation time for models with many random effects. The
StaticArrayspackage is now used for vectors only whereas normal arrays are used for matrices.
- It’s now possible to fit models with a
@randomblock using the
- Define an
empirical_bayes_distmethod to compute the empirical Bayes estimates with uncertainties for a fitted model.
- Improve error message when all values of a covariate are missing for a subject.
- Bootstrapping via a new
empirical_bayesfunction now computed the post hoc estimates when using the
NCAReportfunction now returns a
DataFrameinstead of an
FittedPumasModelhas been deprecated in favor of
SimulatedObservationswhen the model doesn’t include events.
- Change of steady-state event calculation from integer to floating point division to work around breaking changes for integer division in Julia 1.4.