midasr 0.8
==========
Bugfix release to accomodate M1 architecture

midasr 0.7
===========
Major release with new functionality.

* Add 5 new models. LSTR-MIDAS and MMM-MIDAS are fitted with midas_nlpr.
SI-MIDAS and PL-MIDAS are fitted with midas_sp. Quantile MIDAS model are fitted 
with midas_qr.

* Add new midas lag implementation mlsd. It allows using time series attributes
of the data and can align high frequency into low frequency when the number of 
high frequency periods is not the same for all low frequency periods.

* Add support for texreg package. Fitted midasr models can be outputed to nice
tables

midasr 0.6
===========
A bug fix release

* Add a CITATION referencing JSS article

* Fix a few minor bugs in documentation


midasr 0.5
============
A bug fix release

* Adapt to new CRAN policy, where all the functions not belonging to base must
be imported.

* Add generalized exponential lag specification function

* Fix a small bug in forecast.midas_r, now correct start is set when time series
info is added.

* Start using testthat for testing the code.

* Do not lose time series information in forecast

midasr 0.4
============
* Remove unexported code

* Remove confusing functions dedicated for working with various parameters of MIDAS regression. All of the parameters can now 
be accessed with coef method. By default coef returns the parameters of NLS problem.  With option midas=TRUE, coef returns the 
MIDAS weights.

* Add function plot_midas_coef for graphical inspection of MIDAS weights.

* Refactor simulation code, so it is more like arima.sim.

* Make forecast return the object of class forecast. Add an option of calculating prediction intervals for forecasts. Currently 
they are computed via simple bootstraping. This allows using print, summary and plot methods from package forecast.

* Move to more consistent naming convention a la Hadley Wickham, this means that dot (.) is only used for S3 method dispatch. If there is 
no S3 dispatch, the underscore (_) is used. This changes a lot of functions, such as hAhr.test -> hAh_test, all the gradient functions,
so revisit your code for possible breakages.

* Add ability to pass user defined weight gradients. See the documentation for weight_gradients option for midas_r function.


midasr 0.3
============
* Add experimental support for nonparametric MIDAS, see Breitung et al. http://www.ect.uni-bonn.de/mitarbeiter/joerg-breitung/npmidas

* Add dependency and support to the package optimx. This gives more flexibility in chosing the optimisation method. 

* Add data sets needed for midasr user guide.

* Add GPL-2 licencing

* Add function midas_r_simple for fitting MIDAS regressions without formula interface. It can be considerably faster than midas_r. 

midasr 0.2
============
A bug fix release

* Fix a bug, where the midas_r_ic_table would not work with formula containing only one term

* More explicit error messages are displayed when optimisation fails or when robust covariance matrix cannot be calculated

* Add progress indicator to average_forecast function

* Fix a bug in data_to_list function, make forecast.midas_r work with midas_u objects.

* Fix a bug in average_forecast, rolling samples were turning to recursive ones, thanks to Michael Swan for pointing it out.


midasr 0.1
============
Release to CRAN.


midasr 0.0.8
============

* Milestone release. 

* Support for the lagged dependent variable. 

* Support for the forecasting and forecast combinations. 

* Support for the model selection based on the information criteria

* First version of the user guide


midasr 0.0.6
============

* Milestone release. Full-fledged support for fitting MIDAS regressions without lagged dependent variable. 

* Robust and non-robust tests for MIDAS restriction. 

* The fitted model object has methods for generic functions `coef`, `deviance`, `fitted`, `residuals`, `predict`, `print`, `residuals`, `summary` and `vcov`. Additionally it has method `logLik` for use with `AIC` and `BIC` and methods `estfun` and `bread` for use with `vcovHAC` from package sandwich, i.e. there is support for computing robust standard errors for the coefficients

* Three demos illustrating the use of the package. They reproduce the results from the article "The statistical content and empirical testing of the MIDAS restrictions". 

midasr 0.0.2
============

* Support for exogenous variables. Supply using `exo` parameter.

* Option for using numerical gradients. New dependency on package numDeriv. 


midasr 0.0.1
============

* Initial release. Very basic functionality. Currently only one predictor variable
  is allowed. Full functionality of the package is reflected in help page of
  function hAh.test.

