fDMA ver. 2.2.6 (Release date: 2020-07-16)
==============

Changes:

* altf() and altf2() corrected to consider out-of-sample forecasts.
* Markov Switching Models dropped from altf().
* Vignette changed.


fDMA ver. 2.2.5 (Release date: 2020-04-03)
==============

Changes:

* guarantee of positive-definiteness after updating fixed in tvpcpp().
* small syntax corrections.


fDMA ver. 2.2.4 (Release date: 2018-09-29)
==============

Changes:

* Changes to follow STRICT_R_HEADERS via Rcpp made.
* Small corrections in computations of coefficients in tvp() and tvpcpp() made.
* Forecast computation of DMA-E in Dynamic Occam's Window method corrected.
* Adding a small constant to posterior model probabilities fixed.
* Setting initial values of variances fixed.
* Google probabilities computations fixed and allowed to cover only selected period.
* Small changes in fDMA() to increase the speed of computations made.
* Option to specify the number of cores used in parallel version of fDMA() added.
* Alternative averaging schemes added to fDMA().
* forced.models, forbidden.models and forced.variables arguments added to fDMA().
* reduce.size() added.
* coef(), fitted(), predict(), residuals() and rvi() for "dma" object added.
* Non-interactive option to plot methods added.
* Small changes to improve the performance added.


fDMA ver. 2.2.3.1 (Release date: 2018-06-29)
==============

Changes:

* Small correction in NAMESPACE 


fDMA ver. 2.2.3 (Release date: 2018-01-28)
==============

Changes:

* Vignette added.
* Possible zero posterior model probabilities corrected in fDMA() and altf4().


fDMA ver. 2.2.2 (Release date: 2018-01-21)
==============

Changes:

* Exponentially weighted moving average variance updating corrected.


fDMA ver. 2.2.1 (Release date: 2017-11-24)
==============

Changes:

* Warnings of replacing previous imports between Rcpp and utils corrected.


fDMA ver. 2.2 (Release date: 2017-11-15)
==============

Changes:

* Crucial part, i.e., tvp(), rewritten in C++ to increase the speed of computations.


fDMA ver. 2.1 (Release date: 2017-10-12)
==============

Changes:

* Dynamic Occam's Window extended to work even if not all possible models with a constant are used.
* Limit of number of models used by Dynamic Occam's Window added.
* Option to print during computations with Dynamic Occam's Window the number of currently computed recursive DMA round and the number of models used in this round added.
* grid.DMA() fixed to work with multiple lambda values.
* Google probabilities computations fixed.
* NA coefficients in altf() and altf2() fixed.
* Problem with constant x fixed in tvp().
* Plotting posterior model probabilities in plot.dma() fixed.
* Akaike Information Criterion with a correction for finite sample sizes (AICc) added to rec.reg(), roll.reg(), altf2() and altf3().
* Google probabilities added to altf2().
* altf3() and altf4() fixed to work with models with constant only.
* Relative variable importance and expected number of variables added to altf2() and plot.altf2().
* More outcomes summary added to summary.altf2().
* Expected window size added to altf3(), altf4(), plot.altf3() and plot.altf4().
* descstat() for basic descriptive statistics added.
* standardize() added to rescale variables to have mean 0 and standard deviation 1.
* onevar() added to quickly create a matrix indicating one-variable models.
* archtest() outcomes changed to "htest" class.


fDMA ver. 2.0 (Release date: 2017-08-31)
==============

Changes:

* Engle's ARCH test added.
* Forecast accuracy tests added.
* A few stationarity tests added.
* grid.roll.reg() (as "grid.roll.reg" object) for roll.reg() with various windows added.
* rec.reg() for recursive regression added.
* roll.reg() outcomes as an object of "reg" class.
* Akaike Information Criterion, Bayesian Information Criterion and Mean Squared Error added to roll.reg() outcomes.
* Regression coefficients and p-values for t-test for regression coefficients added to roll.reg() outcomes.
* roll.reg() fixed to work also with constant only.
* grid.tvp() (as "grid.tvp" object) for tvp() with various lambdas added.
* Predicitive density and estimated regression coefficients from all periods added to tvp() outcomes.
* Exponentially weighted moving average variance updating added to tvp().
* tvp() changed in order to work inside fDMA(), outcomes as "tvp" class.
* altf4() for averaging over different windows sizes for a time-varying parameters rolling regression added.
* altf3() for averaging over different windows sizes for a rolling regression added.
* alft2() for model averaging alternative forecast added.
* More outcomes added to altf().
* Direct comparision of a "dma" class object with alternative forecast added to altf().
* Option to choose which alternative forecast will be computed by altf() added.
* For forecast quality measure Mean Squared Error replaced by Root Mean Squared Error.
* Number of models used in Dynamic Occam's Window method and posterior model probabilities added to plot().
* "inc" display in summary() of fDMA() outcomes fixed.
* Predicitive densities from the last period added to fDMA() outcomes.
* fDMA() upgraded to work better with parallel computations on Windows machines.
* Setting the initial values of variance for the models equations in fDMA() fixed.
* Estimation of models without constant fixed.


fDMA ver. 1.1 (Release date: 2017-07-11)
==============

Changes:

* Plot's menu fixed.


fDMA ver. 1.0 (Release date: 2017-07-09)
==============

* Initial release.

