FS-DAM performs feature extraction through latent variables identification. Implementation is based on autoencoders with monotonicity and orthogonality constraints.

Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)), for multivariate analysis
    (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression.
    The distinctive feature of our package is the possibility of
    monitoring the statistics of interest as a function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.

Functions for selecting attributes from a given
    dataset. Attribute subset selection is the process of identifying and
    removing as much of the irrelevant and redundant information as
    possible.

'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support.

Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model.