minor update to

`plot_effects()`

function to increase flexibility of plotting different quantile valuesminor 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 combinationsaddition of the

`plot_paths()`

plotting function to plot the standardized coefficient values through the epsilon-telescopeaddition of the

`predict.smoothic()`

major update to the

`smoothic()`

function to include different families of distributionsaddition 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`

datanew dataset

`diabetes`

initial release

two datasets

`bostonhouseprice`

and`sniffer`

automatic variable selection using the

`smoothic`

functioncan choose between distributional regression (multi-parameter) with

`model = "mpr"`

and location-only regression (single parameter) with`model = "spr"`