Train and make predictions from a multi-layer perceptron neural
        network with optional partial monotonicity constraints.

Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) 
	development. All functions handle both binary and continuous target variable. 
	Functions that use isotonic regression in the first stage of binning process have an additional 
	feature for correction of minimum percentage of observations and minimum target rate per bin. 	
	Additionally, monotonic trend can be identified based on raw data or, if known in advance,
	forced by functions' argument. Missing values and other possible special values are treated 
	separately from so-called complete cases.

This is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) 
	     that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning 
	     of numeric risk factors when building credit rating models. The additional functions implemented in 
	     'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. 
	     The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, 
	     while special case and outlier imputation functions are written to be used with shiny UI.

Generate a monochrome palette from a starting colour 
    for a specified number of colours. The package can also be used to display 
    colour palettes in the plot window, with or without hex codes and colour labels.

Implementation of the Monothetic Clustering
    algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
    continuous data sets. A lot of extensions are included in the package,
    including applying Monothetic clustering on data sets with circular
    variables, visualizations with the results, and permutation and
    cross-validation based tests to support the decision on the number of
    clusters.