Release Notes for Pumas 2.6.1

Pumas

Features and significant changes

Bug fixes and minor improvements

  • Fix formatting of infer(..., SIR) docstring
  • Fix the check for zero gradients in matrix domains used for variance-covariance matrices such that they successfully throw and report the correct index.
  • Fix a bug where simulation of non-DiffEq models with covariate interpolation but without events would fail.
  • Fix a bug that would cause an error to be thrown when fitting subjects that had ranges such as 0:1:24 provided as their obstimes.
  • Fix warning message for duplicated BayesMCMC options given in both the type and as keyword arguments to fit. Previously, the options used was not correctly reflected in the warning.
  • Use provided parameters in zero_randeffs to construct the distributions of the random effects instead of the default values in the model.
  • Remove trailing whitespace in show of PumasEMModel
  • Remove trailing whitespace in show of PumasModel
  • Use median for initialization of MvLogNormal instead of mean for consistency with LogNormal.
  • Use mean/mode for initialization of Wishart/InverseWishart.
  • Fix constantcoef use in covariate_select when passed as NamedTuple. Previously, the input values would be ignored. Note, the preferred method is to input the parameters to be used in the param input and specify the constant parameters only as a tuple of symbols.
  • Allow for MAP in findinfluential. Previously, the function would not allow MAP due to a type restriction.
  • Fix a bug where randeffs input to functions would not allow for a different ordering of the random effects than the ordering found in the model.
  • Fix DataFrameconstructor of SimulatedObservations when some subjects have events defined and others do not.
  • Fix a bug where the variables defined in @vars would not be in @observed if @init had been specified.
  • Fix infer with MarginalMCMC and constantcoef. Previously, this would fail.
  • Fix show for likelihood approximations such as FOCE, LaplaceI etc. Previously, these would be way to verbose when showing the results of fit, when storing approximation information in DataFrames etc.
  • Error if variables defined in @observed and @derived use names that are already used to define variables in other blocks.
  • Throw useful error when cholesky decomposition fails as part of MvNormal construction. For example, this could happen when calling simobs with infer input.
  • Add docstrings for SimulatedPopulation and SimulatedObservations.
  • Fix @delay with integer obstimes.
  • Fix inspect for JointMAP. Previously, this would fail with a method error.
  • Fix DataFrame construction when route is part of the model. Previously, the route would be filled in an inconsistent manner that caused output of our DataFrame constructors to produce content that could not be read by read_pumas.
  • Fix CorrDomain constructors.
  • Allow subjects to have different number of observations in ByObservation Bayesian cross-validation.
  • Fix a bug where parameters would not always be transformed back to the original parameter domain in some Bayesian diagnostics methods.
  • Improve error message when no subject observations are in the model and fix some diagnostic functions when some observations are not in the model. This applies to situations where there are more observed variables in the subject than there are observed variables being modelled in the @derived block.
  • Fix parameter elimination in MTK systems with units.
  • Fix loglikelihood for MAP and JointMAP to correctly exclude the contribution of the prior.

Optimal Design

  • Fix specifying initial sampling times as a vector that doesn't contain vectors.
  • Changed the optimization verbose option default to true.
  • Fixed the rendering of design docstring.

Features

Bug fixes and minor improvements

Pumas Utilities

Features

Bug fixes and minor improvements

Bioequivalence

Features

Bug fixes and minor improvements

  • Fix rendering of markdown table in docstring of pumas_be.
  • Fix confidence interval in output table of reference scaled analyses
  • Allow DataFrame inputs with abstractly typed columns

NCA

Features

Bug fixes and minor improvements