A 'future' backend that enables seamless execution of parallel R
    workloads on 'Amazon Web Services' ('AWS', <https://aws.amazon.com>),
    including 'EC2' and 'Fargate'. 'staRburst' handles environment
    synchronization, data transfer, quota management, and worker orchestration
    automatically, allowing users to scale from local execution to 100+ cloud
    workers with a single line of code change.

'This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.'

A robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2024), <doi:10.1371/journal.pgen.1011423>].

Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.

Produces publication-quality LaTeX, ASCII, and HTML regression
    tables. Designed as a drop-in replacement for the 'stargazer' package
    for 'lm' and 'glm' models, with added native support for 'fixest',
    'plm', and 'alpaca' objects including automatic fixed-effect and
    random-effect indicator rows and SE-type detection. Standard errors can
    be supplied as variance-covariance matrices, numeric vectors, or
    auto-extracted from supported model objects. Follows the interface of
    Hlavac (2022) <https://CRAN.R-project.org/package=stargazer>.