# uGMAR 3.1.0

- Added a
`NEWS.md`

file to track changes to the package.
- New exported functions: get_regime_means, get_regime_autocovs, get_regime_vars, uncondMoments, get_soc, condMoments, stmarpars_to_gstmar, stmar_to_gstmar.
- Generally more functionality for conditional and unconditional moments, and convenient tools for switching to G-StMAR model from a StMAR model.
- Implemented an algorithm by Monahan (1984) to the genetic algorithm for more thorough search of the parameter space near boundaries of the stationarity region.
- simulateGSMAR now provides better tools for forecasting. This update includes non-backward compatible changes for the return values if the argument ntimes is set to be larger than one. In additional to the samples, it now returns a list containing the mixing weights and component that was used to generate each observation.
- The arguments nCalls and nCores in fitGSMAR are now changed to ncalls and ncores for consistency.
- Fixes on minor bugs that used to cause errors in some special cases.

- Updates on documentation
- Added inflation expectation data (IE)

# uGMAR 3.2.0

- In the predict method arguments “ci” and “ci_type” were changed to “pi” and “pi_type” to signify “prediction interval”" as it’s more correct expression than “confidence interval”. Also the default prediction method is now median, and not mean.
- Changed the default number of CPU cores employed by the estimation function fitGSMAR to be at most two due to CRAN policy.
- Added the argument “seeds” to fitGSMAR allowing one to set the random number generator seed for each call to the genetic algorithm.
- Finite difference approximations for differentials regarding overly large degrees of freedom parameters now give reasonable approximation instead of numerical error.
- The maximum value for degrees of freedom parameters is now 1e5.
- New exported function alt_gsmar that conveniently constructs a GSMAR model based on an arbitrary estimation round of fitGSMAR.
- New exported function get_foc which is the same as get_gradient but with convenient name.
- The default number of generations in the genetic algorithm is now 200 (was min(400, max(round(0.1*length(data)), 200)) before).
- In various functions, user may now adjust the difference ‘h’ used in the finite difference approximations for differentials of the log-likelihood.
- Bug fix: the summary print for gsmar objects falsely displayed standard error for the non-parametrized mixing weight
- Fixed typos etc. in documentation.