Functions and a graphical user interface for graphical described
    multiple test procedures.

A lightweight fork of 'gMCP' with functions for graphical
    described multiple test procedures introduced in
    Bretz et al. (2009) <doi:10.1002/sim.3495> and
    Bretz et al. (2011) <doi:10.1002/bimj.201000239>.
    Implements a flexible function using 'ggplot2' to create
    multiplicity graph visualizations.
    Contains instructions of multiplicity graph and graphical testing for
    group sequential design, described in
    Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>,
    with necessary unit testing using 'testthat'.

A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.

Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.

Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).

Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
    Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
    that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
    In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
    selecting an optimal structure of model or network.
    The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.