An interface for performing all stages of 'ADMIXTOOLS' analyses
    (<https://reich.hms.harvard.edu/software>) entirely from R. Wrapper functions
    (D, f4, f3, etc.) completely automate the generation of intermediate
    configuration files, run 'ADMIXTOOLS' programs on the command-line, and
    parse output files to extract values of interest. This allows users to focus
    on the analysis itself instead of worrying about low-level technical details.
    A set of complementary functions for processing and filtering of data in
    the 'EIGENSTRAT' format is also provided.

Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM).
    See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) <arXiv:1904.03272>.

Estimate age-depth models from stratigraphic and sedimentological data,
    and transform data
    between the time and stratigraphic domain.

Bayesian inference using the no-U-turn (NUTS) algorithm by 
 Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. 
 Designed for 'AD Model Builder' ('ADMB') models,
 or when R functions for log-density and log-density gradient
 are available, such as 'Template Model Builder'
 models and other special cases. Functionality is similar to 'Stan', 
 and the 'rstan' and 'shinystan' packages are used for diagnostics and 
 inference.

Connect to the 'Adobe Analytics' API v2.0 <https://github.com/AdobeDocs/analytics-2.0-apis>
             which powers 'Analysis Workspace'. The package was developed
             with the analyst in mind, and it will continue to be
             developed with the guiding principles of iterative,
             repeatable, timely analysis.

Optimize one or two-arm, two-stage designs for clinical trials with 
    respect to several implemented objective criteria or custom objectives.
    Optimization under uncertainty and conditional (given stage-one outcome) 
    constraints are supported.
    See Pilz et al. (2019) <doi:10.1002/sim.8291> and 
    Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.