greybox v0.3.1 (Release data: 2018-09-07)
==============

Changes:
* Corrected some typos in README.md and added description of several functions.
* predict() and forecast() functions now produce confidence and prediction intervals for the provided holdout sample data. forecast() is just a wrapper around predict().
* Normal and log-normal distributions are now available in alm().
* rmc() now uses alm().
* stepwise(), lmCombine() and lmDynamic() can now also be constructed with distributions from alm(). They use lm() in case of "dnorm" and alm() otherwise.
* alm() now does not return vcov if you didn't ask for it (should increase speed of computation for large datasets).
* alm() can be constructed with the provided vector of parameters (needed for vcov method).
* We now use well-known analytical solutions for the cases of distribution="dnorm" of alm() and other functions.
* Code of lmCombine and lmDynamic is slightly simplified.
* We now use Choleski decomposition for the calculation of the inverse of matrices in alm.
* distribution="dlogis" is now available for alm().
* alm() now also supports logit and probit models, which are called using distribution="plogis" and distribution="pnorm" respectively (reference to the names of respective CDFs in R).
* alm() now has occurrence parameter, which allows dealing with zeroes in the data. In this case, a mixture distribution can be used.
* alm() with dlnorm now also returns analytical covariance matrix instead of hessian based one.
* stepwise(), lmCombine() and lmDynamic() now rely on .lm.fit() function, when distribution="dnorm", so the speed of calculation should be substantially higher.
* New functions for class checks: is.greybox(), is.alm(), is.greyboxC(), is.greyboxD(), is.rmc() and is.rollingOrigin().
* stepwise() now calculates only the necessary correlations. This allows further inceasing the speed of computation.
* alm() uses its own mean function, so this should also increas its speed.
* Correct prediction intervals for the model with the occurrence part and a new parameter in prediction function - side - which allows producing one-sided PIs.
* stepwise() should now work better with big data.
* Futher optimisation of stepwise in order to decrease the used memory.
* alm() and all the other functions now return "data" instead of "model" and don't produce terms and qr. This should save some space.
* vcov.alm() now uses call in order to reestimate the model.
* rmc() now returns groups of methods. This can be used for analytical purposes.
* alm() now uses a more refined parameters for vcov calculation for "dchisq" and returns a slightly different call with vcov.
* pointLik.alm() method for alm class.
* alm() now extracts meaningful residuals depending on the distribution used. e.g. dnorm -> y - mu, dlnorm -> log(y) - mu
* stepwise() now allows defining occurrence model. So now you can do something like: stepwise(ourData, distribution="dlnorm", occurrence=stepwise(ourData, distribution="plogis"))
* predict function now returns probabilities for the lower and upper intervals. So if you had side="upper", then the lower will be "0", and the upper will be the specified level.
* dpois and dnbinom distributions in alm. alm() allows producing prediction intervals for both of them. But covariance matrix of parameters for dnbinom might be hard to calculate...
* The dispersion parameter of dnbinom in alm() is now estimated separately, which now solves a lot of problems.
* Renamed parameter A into B for alm(). Very serious thing!
* distribution="dchisq" in alm() now estimates the non-central Chi Squared distribution. The returned scale corresponds to the estimated number of degrees of freedom, while mu is the exponent of the expectation.
* rmc() now colours the lines depending on the number of groups. If there's only one, then there's one group and the differences are not significant.
* Started a new vignette for the alm() function.
* graphmaker() is now moved from smooth to greybox.

Bugfixes:
* Fixed a bug with the style="line" in rmc(), where the grouping would be wrong in cases, when one method significantly differs from the others.
* logLik previously was not calculated correctly for the mixture models.
* Bugfix in hessian calculation, when Choleski decomposition works...
* Bugfix in pointLik for the models with occurrence.
* predict() function failed with newdata with one observation.
* Initials of both Poisson and NegBin in case of non-zero data are now taken with logs. This leads to more robust starting points.


greybox v0.3.0 (Release data: 2018-08-05)
==============

Changes:
* New cool function - lmDynamic() - that constructs a dynamic linear regression based on point ICs.
* New set of functions for Folded normal distribution.
* New function - alm - Advanced Linear Model.
* Folded normal distribution for rmc() with value="a".
* Proper model for chi-squared distribution in alm and rmc.
* Renamed distributions in the alm function.

Bugfixes:
* determination() function did not work in cases of 2 variables.
* vcov() and confint() were misbehaving when nVars==1.

greybox v0.2.3 (Release data: 2018-08-02)
==============

Changes:
* determination() now automatically drops variables with no variability.
* New function - nemenyi() - imported from TStools with minor bugfixes and corrections.
* It appears that Nikos is against the move of nemenyi() function from TStools to greybox. This was a misunderstanding between the two of us. So no nemenyi() function here, nothing to see here, move along!
* New function for multiple comparison of methods based on regression analysis - rmc(). This is a parametric analogue of nemenyi test. The function works with errors, their absolute and squared values and relies on lm / glm.
* New methods imported from smooth: errorType, pointLik and pAIC.

Bugfixes:
* plots of ro() were misaligned in case of co=FALSE
* ro() now also returns the correct actual values (previously they could be cut off when ci=FALSE).


greybox v0.2.2 (Release data: 2018-05-25)
==============

Changes:
* New description of the package and badges in README.md
* New function - determination() - returns R-squares for the provided data. This can be useful when you need to analyse the multicollinearity effect.
* nParam method for logLik class.
* BICc - new method for the classes, implementing, guess what?
* Updated description of the package in the help file.
* ro() now returns a class and has print and plot methods associated with it.
* ro() is much more flexible now, returning whatever you want in an adequate format.
* New methods for the greybox functions: confint, vcov.
* Renamed "combiner" into "lmCombine", because it makes more sense. We will use "combine" name for a more general function that would combine forecasts from arbitrary provided models (e.g. smooth, forecast and lm classes).

Bugfixes:
* sigma() method returned the wrong standard error in cases of combined models.


greybox v0.2.1 (Release data: 2018-05-01)
==============

Changes:
* New description of the package and badges in README.md


greybox v0.2.1 (Release data: 2018-05-01)
==============

Changes:
* print.summary now specifies digits. Summary does not round up anything. This corresponds to the normal behaviour of these methods.
* Implemented Laplace distribution, which is useful when models are estimated using MAE.
* Sped up qs() and qlaplace() functions using the inverse cumulative functions.
* New function - ro() - Rolling origin.

Bugfixes:
* qs() returned weird values when several 0 and 1 were specified as probabilities.

greybox v0.2.0 (Release data: 2018-03-10)
==============

Changes:
* combiner now uses a more clever mechanism in case of bruteForce==FALSE.
* combiner now also checks if the provided data has ncol>nrow and sets bruteForce if it has.
* Use Kendall Tau as default in cor() for stepwise.
* Don't use Kendall Tau as default everywhere - only for fat regressions.
* New summary and print methods for models from stepwise. No statistical tests printed, only confidence intervals and ICs.
* AICc for smooth functions in case of iSS models should take only the demand sizes into account, not all the parameters.


greybox v0.1.1 (Release data: 2018-03-05)
==============

Changes:
* We now do not depend on smooth. We suggest it. It's smooth that should depend on greybox!
* New function imported from smooth - AICc.
* New functions for the S distribution (the maximisation of likelihood of which corresponds to the minimum of HAM): ds, ps, qs, rs.
* stepwise now returns the object of two classes: greybox and lm.
* combiner now returns three classes: greybox, lm and greyboxC.
* nParam is moved to greybox from smooth.

Bugfixes:
* If smooth is not installed, plot forecasts using simpler function.
* The forecasts are now produced for the combined models in cases of fat regressions.


greybox v0.1.0 (Release data: 2018-03-03)
==============

* Initial release. stepwise() and xregExpander() are imported here from smooth package.
* combiner() function that combines lm() models. This thing is in the development right now.
* combiner() has a meaningful summary() now. Working to make it more accesible to lm functions.
* summary() for combiner now returns the list of values.
* stepwise() should now perform slightly better.
* combiner() can now be smart and use stepwise for the models pool creation.
* combined lm model can now be used together with predict() and forecast() functions.
* plot() and forecast() methods for the combined functions.
