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

Estimates a penalized precision matrix via the alternating direction method of multipliers (ADMM) algorithm. It currently supports a general elastic-net penalty that allows for both ridge and lasso-type penalties as special cases. This package is an alternative to the 'glasso' package.
    See Boyd et al (2010) <doi:10.1561/2200000016> for details regarding the estimation method.

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

Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) <doi:10.1098/rstb.2019.0723>.

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.