Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. 
    The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.

Fits from simple regression to highly customizable deep neural networks 
    either with gradient descent or metaheuristic, using automatic hyper parameters 
    tuning and custom cost function.
    A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.

A tool that improves the prediction performance of multilevel
    regression with post-stratification (MrP) by combining a number of machine
    learning methods. For information on the method, please refer to Broniecki, 
	Wüest, Leemann (2020) ''Improving Multilevel Regression with 
	Post-Stratification Through Machine Learning (autoMrP)'' in the 
	'Journal of Politics'. Final pre-print version: 
	<https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.

Automatically generate a changelog file (NEWS.md /
    CHANGELOG.md) from the git history using conventional commit messages
    (<https://www.conventionalcommits.org/en/v1.0.0/>).

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses.
              We rely on the 'pamr' <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the 
              least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.