Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels.

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.

Provides tools to summarize, analyze, and visualize results
    from Mendelian randomization studies using summarized genetic
    association data. The package includes functions for generating forest
    plots and scatter plots at the single-nucleotide polymorphism
    and Mendelian randomization method levels, and for fitting multiple
    estimators in a unified pipeline, including inverse-variance weighted
    estimation, Mendelian randomization Egger regression,
    the weighted median estimator, the robust adjusted
    profile score, Mendelian randomization pleiotropy residual sum
    and outlier, Mendelian randomization with the genotype
    recoding invariance property, and a Bayesian horseshoe
    method. Related methods are described by Burgess (2013)
    <doi:10.1002/gepi.21758>, Bowden (2015) <doi:10.1093/ije/dyv080>,
    Bowden (2016) <doi:10.1002/gepi.21965>, Zhao (2020)
    <doi:10.1214/19-AOS1866>, Verbanck (2018)
    <doi:10.1038/s41588-018-0099-7>, Dudbridge (2025)
    <doi:10.1371/journal.pgen.1011967>, and Grant and Burgess (2024)
    <doi:10.1016/j.ajhg.2023.12.002>. Related open-source software
    includes 'TwoSampleMR' <https://github.com/MRCIEU/TwoSampleMR>,
    'mr.raps' <https://github.com/qingyuanzhao/mr.raps>,
    'MR-PRESSO' <https://github.com/rondolab/MR-PRESSO>, and
    'MR-Horse' <https://github.com/aj-grant/mrhorse>.