A new release of Pumas is now available. You can find a link to the latest version at our download page. The new version we release is v1.1. This is a feature release that is fully backward compatible with older versions of Pumas. If something no longer works as it did in previous versions, please report the issue here on this forum.
Release Notes for Pumas v1.1.0
Features and improvements
- Adjusted several error messages have been improved to better guides users to correct mistakes in input data
- Add MAP (Maximum a Posteriori) estimation using the
infernow accepts a second positional argument
Pumas.SIR()to calculate standard errors using either bootstrap or Sampling Importance Resampling (SIR).
Truncatedtypes can now be used to handle incomplete data such as datasets with observations below the limit of quantification (BLQ) when fitting with the
- Make asymptotic variance-covariance matrix calculations multithreaded for faster
- Add progress meters to
inferto help assess the time left in the calculations.
- Add a returncode to
SimulatedObservations returned from
simobsto allow for some simulated subjects to fail due to extreme random effects without losing all other simulated subjects because an error is thrown
- When multiple dependent variables are present
wresidualsnow returns weighted residuals for all variables with a
Normaldistribution and ignores other types of variables.
- Expose the
BayesMCMCinference to allow users to fine-tune the target acceptance rate to their problem
- Implement a compact
Eventfor improved printing of
- VPC plots now clearly indicate in the title what variable is being stratified on
- When a parameter was found to be potentially unidentified it would sometimes report the wrong parameters when matrix domains
- When simulating subjects with
simobsand a vector of parameters the subjects and parameters were incorrectly combined.
read_pumasdid not correctly error if
evidwas 1 and
- A PumasModel could not be defined and used inside a function
- It was unintentionally possible to use
@observedblock and right-hand sides
=in the same block could be distributions.
- Variables from the
@varsblock were not available in
@dynamicsif the PumasModel had no
- Printing the results of a
fitwould throw an error if a parameter had become
amtwould not be set to
missingwhen constructing a
- Evaluating the
@derivedblock would sometimes allocate too much memory when no covariates were present in the model
- When multiple dose events were present and NCA variables were calculated in the
DataFrameconstructor would incorrectly tabulate the NCA variables when called on a
LAPACKException(2)when the selected bandwidth was too small instead of calculating the well-defined unsmoothed VPC.
- Dose control parameters would not be correctly promoted to the appropriate types in all cases leading to errors thrown from the automatic differentiation code.
- Using reset and reset and dose events (evid 3 and 4) would sometimes throw an error if the same times after dose were present more than once.
DataFrameconstructor would throw an error if called on the results of
predictif the keyword
obstimeswas different than the observed times in the subjects.
- Fix a corner case where
wresidualswould fail if a proportional error model had an observation at time 0.
- NCA supported up to v1.0.2
- QuantileRegressions v0.1.4 now used in Pumas
- No longer depend on Query for tests