Maintainer: Samuel V. Scarpino <s.scarpino@northeastern.edu>

person("Ben", "Marwick", email = "benmarwick@gmail.com", role = c("aut", "cre")),
person("Amber", "Johnson", email = "ajohnson@truman.edu", role = "aut"),
person("Doug", "White", email = "drwhite@uci.edu", role = "aut"),
person("E. Anthon", "Eff", email = "Anthon.Eff@mtsu.edu", role = "aut")
Description: Binford's hunter-gatherer data includes more than 200 variables
coding aspects of hunter-gatherer subsistence, mobility, and social organization
for 339 ethnographically documented groups of hunter-gatherers.

Title: Estimates Parameters of Functions Driving Binomial Random
Description: Provides maximum likelihood estimates of the
performance parameters that drive a binomial distribution of observed
errors, and takes full advantage of zero error observations. High performance
communications systems typically have inherent noise sources and other
performance limitations that need to be estimated. Measurements made at
high signal to noise ratios typically result in zero
errors due to limitation in available measurement time. Package includes
theoretical performance functions for common modulation schemes (Proakis,
"Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted
functions to work with the performance functions.
Author: Philip Shea [aut, cre] (<https://orcid.org/0000-0001-5460-9987>)

Title: Get Bing Ads Data via the 'Windsor.ai' API
Description: Collect  your data on digital marketing campaigns from bing Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

Author: Terrence Brooks, Berkley Shands, Skye Buckner-Petty, Patricio S. La Rosa, Elena Deych, William D. Shannon
Description: This package provides tools to analyze binary graph objects.

Imports: matrixStats, network, gplots, genalg, doParallel, foreach,
Description: Tools to analyze binary graph objects.

Imports: matrixStats, network, gplots, doParallel, foreach, parallel,

Title: Haar-Fisz functions for binomial data
Depends: R (>= 2.10), wavethresh, adlift (>= 0.9.2)
Description: Binomial Haar-Fisz transforms for Gaussianization