Changes from Version 1.0.4 to 1.0.4-1:
* Corrected R dependency from 3.0.0 to 3.1.0, which is needed for some package tests.
* Adjusted tolerance parameters in tests for better portability (test should now only fail in case
of clearly bogus answers).
Changes from Version 1.0.3 to 1.0.4:
* Tweaked the underlying algorithms for increased numerical stability of filtering and smoothing
in KFS. Note that it is still possible that exact diffuse initialization fails due to to numerical
issues whereas traditional 'big value' approach works and vice versa.
* Corrected a bug in residuals.KFS which threw an error when computing recursive residuals without
diffuse initialization.
* Corrected output of LogLik method for non-Gaussian models: It now returns -Inf only when the
approximation algoritm failedcompletely (resulting NA), and issues only warning about
non-convergence in other cases.
* Added checks of degenerate model to LogLik method. If all elements in R, Q and H/u are zero, or
they contain any non-finite values, -Inf is returned.
* Fixed a bug in approximation algorithm which caused the approximation to fail for seemingly
random models.
* Fixed a bug in SSMcycle which caused error with common components.
* Fixed bug in SSMcycle which resulted erroneus system matrix T in all cases.
* Fixed a bug in SSMseasonal which caused error in SSModel when using common components.
* SSMseasonal with trigonometric seasonal now works properly when period is odd.
* Fixed a bug in coef.KFS which caused function to return smoothed states even with argument
filtered=TRUE if they were present in KFS object.
* Added argument "maxiter" to predict.SSModel and changed its default value in all functions to 50.
* Corrected a bug in function ldl which caused the decomposition of semidefinite matrices to fail
silently.
* Changed variable mu to m for mean filtering for non-Gaussian models without simulation just like
in other cases.
* Changed convergence criterion in Gaussian approximation algorithm from linear predictor based to
deviance based.
* Properly exported assigment using subset method. See ?subset.SSModel for details.
Changes from Version 1.0.2 to 1.0.3:
* Changed default filtering option for Gaussian models back to "state" (was previously "none").
* Argument "invlink" in KFS is obsolete, proper argument is now "mean". For backwards compatibility,
"invlink" still works.
* Naming of output components of KFS were unified to follow the logic of state filtering and
smoothing: Now mean filtering returns an array m, and mean smoothing returns an array muhat,
signal filtering returns an array t, and signal smoothing returns an array thetahat.
Also, signal filtering and smoothing for Gaussian models returns m and muhat, not t and thetahat.
* Fixed several bugs concerning the linearization of Gamma and Negative binomial distribution,
which resulted false standard errors, among other problems.
* Corrected bug in computing standardization for pearson and deviance residuals,
and added separate method rstandard for standardized residuals.
* Added method hatvalues for computing hat values from KFS output.
* Added method fitted for extraction of fitted values from KFS output.
* Added method coef for extraction of fitted state values from KFS output.
* Added method deviance for computation of deviance from KFS output.
* Mean estimates for Poisson distribution now contains the effect of offset. Note that linear
predictor theta still does not contain offset term, i.e. mu=exp(theta)*offset.
* Fixed a bug in SSMregression. In earlier versions missing values were removed from model matrix
which caused error in constructing system matrices.
* Fixed a bug in SSMregression relating state names when type=="common".
* Fixed a bug in approxSSM which caused the approximation to fail if system matrix Z contained
missing values.
* LogLik.SSModel now produces error if linearization of non-Gaussian model did not converge.
* When using formula list in SSMregression, option to use list of datasets is now also supported.
* Naming convention for common regression coefficients with formula lists was changed.
Names for the states are now taken from the first dataset in data list.
* Fixed a bug in SSModel which caused error when formula contained interaction terms and custom parts.
* Broken SSMcycle with type="distinct" was fixed.
* Fixed a bug regarding simulateSSM with missing observations in diffuse phase, which caused
variances of simulated values to be much larger than expected.
Changes from Version 1.0.0 to 1.0.2:
* Fixed a bug in SSMarima which caused function to fail if there was
no ar part and stationarity option was TRUE.
* Fixed a bug regarding the deviance residuals for Gaussian model.
* Fixed a bugs in logLik.SSModel and transformSSM relating to the model transformation.
* Fixed a bug in KFS signal smoothing regarding the multivariate model with
missing observations with time invariant Z and non-diagonal H.
* SSModel should now keep the time series attributes of the response variable.
* F is now set to 0 if F is smaller than the machine epsilon.
* transformSSM now keeps the proper dimnames of the system matrices.
* Changed the variable mu to muhat so it is similar to alphahat, thetahat etc.
* KFS now always returns the log-likelihood for the Gaussian models.
* Fixed a bug in residuals.KFS concerning the standardized deviance residuals.
* Fixed a bug in predict.SSModel concerning the interval computation for
non-Gaussian models without simulation.
* Fixed a bug in SSMregression and SSMcustom where a test of equality of
integer and double variables was done using function identical instead of ==.
* Fixed a bug in predict.SSModel regarding the standard error computations
without simulation for non-Gaussian models.
* Added filtering for non-Gaussian models.
* Changed the filtering and smoothing options on KFS.
* Added option to simulate from predictive distributions.
* Other minor bug typo fixes.