Version 1.0.8 [2017-07-10]

cvts() now supports parallel fitting through the num.cores argument. Note that if the model that you are fitting also utilizes parallelization, the number of cores used by each model multiplied by num.cores passed to cvts() should not exceed the number of cores on your machine.
The package versioning now follows semantic versioning more closely; however, the convention used will be MAJOR.MINOR.RELEASE_NUM.
Instead of loading the entire ggplot2 namespace, only specific functions are now imported.

Version 0.4.1 [2017-06-18]

The “forecast” package v8.1 now declares the S3 method accuracy(), so this is imported and no longer declared in “forecastHybrid”.

Import the “zoo” package
Fixed a bug in cvts() when using rolling = TRUE whereby the incorrect number of periods were calulated. Thanks to Ganesh Krishnan for the bugfix.
The cvts() function now allows additional arguments to be passed with .... Thanks to Ganesh Krishnan.
Additional ... arguments can be passed to the individual component models in forecast.hybridModel().
Documentation fixes and improvements, particularly for the cvts() function.
Unit tests were optimized for speed, and the package tests in half the previous time.
The behavior of the forecast() function from the “forecast” package when multiple or single prediction intervals are passed has changed. The prediction inervals are now consistently returned as matrices. This change fixes a bug in forecast.hybridModel() when multiple prediction intervals are used.
Fixed a bug with forecast.hybridModel() for ets, nnetar, and stlm component models when the level argument was set to a single value instead of a vector of values.
Fixed warning message for superfluous lists passed to base models in hybridModel()

Prediction intervals are now created for nnetar objects in the ensemble. This should address one aspect of incorrect prediction intervals (e.g. issue #37).
theta models can be added (by including “f” in the models = argument for hybridModel()) and are indeed part of the default - so by default, hybridModel() will now fit six models
accuracy.cvts() is now exported
plot.hybridModel() now supports ggplot2 graphics when the argument ggplot = TRUE is passed.
Time series must be at least four observations long
Fixed an error where e.args was passed to tbats instead of t.args

Add timeseries cross validation with cvts()
Add support for weights = "cv.errors" in hybridModel()
Fix model weights when weights = "insample.errors" and one or more component models perfectly fit the time series
Fixed erroneous warning message when xreg is included in n.args but a nnetar model is not included in the model list
Clean up titles in plot.hybridModel()
Enable passing ... arguments to plot() from plot.hybridModel()
Round weights in print.hybridModel() to three digits for cleaner display
Add verbose argument and enable by default in hybridModel() to display fitting/cross validation progress

Fix broken S3 generic accuracy() and hybridModel.accuracy()
Add vignette
Add NEWS
Remove “fpp” from dependencies
Fix warning for unimplemented parameter weights = "cv.errors"
Fix error with nnetar and stlm models when 2 * frequency(y) >= length(y)
Documentation improvements MORE TODO
Migrate unit tests away from deprecated not() function from “testthat” package
Add additional unit tests for bugfixes (accuracy fix, nnetar/stlm 2 * frequency(y) >= length(y), weights = "cv.errors")