Skip to content

KFAS 1.1.0 released on CRAN

Compare
Choose a tag to compare
@helske helske released this 18 Apr 07:24
· 291 commits to master since this release
v1.1.0
35fb413

Corrected bugs, added some functionality, and tweaked codes for performance and readability.

Changes from version 1.0.4-1 to 1.1.0:

  • Added state types level and slope for easier extraction of states in multiple functions.
  • coef.KFS: Added argument states for partial state vector extraction.
  • simulateSSM: Corrected a bug which gave wrong variances for epsilon disturbances.
  • simulateSSM: Corrected a bug which gave wrong variances for the initial states.
  • rstandard.KFS: Added argument standardization_type which defines whether cholesky or marginal standardization is used.
  • is.SSModel: Tweaked checks for faster performance for time varying models, added check for P1inf.
  • fitSSM: Function now allows fixed time varying covariance matrices.
  • fitSSM: check.model is set to FALSE when calling logLik.SSModel in all cases. see ?fitSSM for details.
  • predict.SSModel: Corrected bug which caused partial signal prediction to fail.
  • predict.SSModel: Corrected bug which caused prediction of non-Gaussian models with time varying u to fail.
  • predict.SSModel: Method now correctly uses original times of the model object for the start and
    end times of the predictions.
  • approxSSM and related functions: Changed the converge criterion for approximating algorithm.
    Previous criterion was missing one term which caused poor (or non-) converge in some cases with non-diffuse states.
  • approxSSM and related functions: Added a line search as part of approximating algorithm.
    This improves the converge of the algorithm especially in case of poor initial values.
  • logLik.SSModel: Changed constant on Gaussian log-likelihood computation so now adding meaningless predictor
    improves diffuse likelihood like it should. In simple regression setting the change is from
    n_log(2_pi) to (n-k)_log(2_pi) where k is the number of regression coefficients. See testLM.R in
    inst/tests for illustration.
  • logLik.SSModel: Added argument 'marginal' for logLik method. If TRUE, additional,
    often constant term suggested by Francke et al. (2010) is added to the diffuse log-likelihood.
    See ?logLik.SSModel for details.
  • logLik.SSModel: Changed default value for check.model to TRUE. For large models this adds small
    overhead but prevents R from crashing with improperly (manually) modified model objects.
  • SSModel: Added terms component for update method.
  • SSModel: Corrected a bug relating to the environments which caused error in SSModel when
    calling it inside a function with index argument defined in nonstandard way.
  • SSModel: When using SSMregression without data argument, if variables are not found in the environment
    of the formula, it now searches them from the data argument of SSModel and from the
    environment of main formula. See examples in ?SSModel.
  • Deprecated subset and 'subset<-' methods for SSModel as these were not in
    line with the base R's generic function. Use '[.SSModel' instead. Generic
    replacement via subset with 'subset<-' was also deprecated as it was only
    used for SSModel object.
  • rtandard.KFS and residuals.KFS: Deprecated deviance residuals.
  • rtandard.KFS and residuals.KFS: Added support for recursive residuals for non-Gaussian models.
  • rtandard.KFS: Corrected Pearson residual formulas for non-Gaussian models.
  • is.SSModel: With na.check=TRUE, function now also checks for extreme values in H and Q (larger than 1e7).
  • Signal filtering now return object t and not theta like it should (see Changes from Version 1.0.2 to 1.0.3).
Assets 2
Loading