It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given.
             The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute).
             This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values.

Predicts any variable in any categorical dataset for given values of predictor variables.
    If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. 
    The user can upload their own datasets and select what variable they want to predict.
    A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.

Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <arXiv:1303.4121>, 
    Geenens and Wang (2018) <arXiv:1602.04862>, 
    Nagler (2018a) <arXiv:1704.07457>, 
    Nagler (2018b) <arXiv:1705.05431>.

Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.

Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.