doParallel, dplyr, foreach, ggplot2, graphics, MASS, methods,
neldermead, parallel, pracma, stats, utils, grDevices

Alternating Direction Method of Multipliers to Solve Dense
Dubmatrix Problem
Brendan Ames <bpames@ua.edu>, Polina Bombina <pbombina@crimson.ua.edu>
Solves the problem of identifying the densest submatrix in a given or sampled binary matrix, Bombina et al. (2019) <arXiv:1904.03272>.

No-U-Turn MCMC Sampling for 'ADMB' and 'TMB' Models
person("Cole", "Monnahan", email = "monnahc@uw.edu", role = c("aut", "cre"))
Bayesian inference using the no-U-turn (NUTS) algorithm by
Hoffman and Gelman (2014) <http://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' ('TMB')
models and other special cases. Functionality is similar to 'Stan',
and the 'rstan' and 'shinystan' packages are used for diagnostics and
inference.

snowfall (>= 1.84.6.1), shinystan (>= 2.5.0), matrixcalc (>=
1.0.3), TMB (>= 1.7.15), stats, knitr, rmarkdown

No-U-Turn MCMC Sampling for 'ADMB' Models
person("Cole", "Monnahan", email = "monnahc@uw.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0871-6700"))
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.
shinystan (>= 2.5.0), matrixcalc (>= 1.0.3), stats, knitr,
TMB, rmarkdown, withr, testthat (>= 2.1.0)
Cole Monnahan [aut, cre] (<https://orcid.org/0000-0003-0871-6700>)

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.
c(
person("Ben", "Woodard", email = "benrwoodard@gmail.com", role = c("aut", "cre")),
person("Tim", "Wilson", email = "timwilson7@gmail.com", role = c("aut", "ctb")),
person("Mark", "Edmondson", role = "ctb")
)
Ben Woodard [aut, cre],
Tim Wilson [aut, ctb],
Mark Edmondson [ctb]