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.

