Intense parallel workloads can be difficult to monitor.
  Packages 'crew.cluster', 'clustermq', and 'future.batchtools'
  distribute hundreds of worker processes over multiple computers.
  If a worker process exhausts its available memory,
  it may terminate silently, leaving the underlying problem difficult
  to detect or troubleshoot.
  Using the 'autometric' package, a worker can proactively monitor
  itself in a detached background thread.
  The worker process itself runs normally,
  and the thread writes to a log every few seconds.
  If the worker terminates unexpectedly, 'autometric' can read and
  visualize the log file to reveal potential resource-related
  reasons for the crash. The 'autometric' package borrows heavily from
  the methods of packages 'ps' <doi:10.32614/CRAN.package.ps> and 'psutil'.

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.