Provides R bindings to the 'Automerge' Conflict-free
    Replicated Data Type ('CRDT') library. 'Automerge' enables automatic
    merging of concurrent changes without conflicts, making it ideal for
    distributed systems, collaborative applications, and offline-first
    architectures. The approach of local-first software was proposed in
    Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019)
    <doi:10.1145/3359591.3359737>. This package supports all 'Automerge'
    data types (maps, lists, text, counters) and provides both low-level
    and high-level synchronization protocols for seamless interoperability
    with 'JavaScript' and other 'Automerge' implementations.

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>.