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.

Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available.

Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.

Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <arXiv:0907.2135>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.