Changes from version 1.1.0 to 1.1.1:
 * Corrected dimensionality checks of systemn matrices in auxiliary functions. Previously SSMtrend 
   threw an error with time varying Q for univariate series.
 * Corrected memory leak in artransform which sometimes caused R to crash.
 * Added tests and example regarding ARIMA models.

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).

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.