NEWS.md
In smoothic: Variable Selection Using a Smooth Information Criterion

smoothic 1.2.0

minor update to plot_effects() function to increase flexibility of plotting different quantile values
minor update to improve the documentation of some functions

minor update to the smoothic() function to include a vector of the maximum iterations to be performed at each epsilon-telescope step (computationally advantageous)
addition of the plot_effects() plotting function to plot the model-based conditional density curves for different covariate combinations
addition of the plot_paths() plotting function to plot the standardized coefficient values through the epsilon-telescope
addition of the predict.smoothic()

major update to the smoothic() function to include different families of distributions
addition of the "smooth generalized normal distribution", where an additional shape parameter is estimated relating to the kurtosis of the error distribution (shape parameter can also be fixed at a user-supplied value)
new option to use nlm() for optimization (optimizer = "nlm") or to use the manually coded Newton-Raphson method (optimizer = "manual")
addition of the Laplace distribution, which corresponds to robust regression where the errors are heavy-tailed
new dataset bostonhouseprice2, which is a corrected version of the original bostonhouseprice data
new dataset diabetes

initial release
two datasets bostonhouseprice and sniffer
automatic variable selection using the smoothic function
can choose between distributional regression (multi-parameter) with model = "mpr" and location-only regression (single parameter) with model = "spr"