These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007)
and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].
Suggests: testthat, covr, knitr, rmarkdown, gsl, lintr (>= 1.0.0)

Title: Bradley-Terry Model with Exponential Time Decayed Log-Likelihood
Authors@R: c(person("Yunpeng", "Zhou", email = "michael.zhou.hku@gmail.com", role = c("aut","cre")),
Description: We apply Bradley-Terry Model to estimate teams' ability in paired comparison data. Exponential Decayed Log-likelihood function is applied for dynamic approximation of current rankings and Lasso penalty is applied for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.

Title: Bias and Uncertainty Corrected Sample Size
Author: Samantha F. Anderson <sander10@nd.edu, Ken Kelley <kkelley@nd.edu>, Scott E. Maxwell <smaxwell@nd.edu>
Description: Implements a method of correcting for publication bias and
uncertainty when planning sample sizes in a future study from an original study.

Author: Samantha F. Anderson <sander10@nd.edu>, Ken Kelley <kkelley@nd.edu>
uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (submitted, revised and resubmitted).

Author: Samantha F. Anderson <samantha.f.anderson@asu.edu>, Ken Kelley <kkelley@nd.edu>
uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

Description: Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and

Title: Hierarchical Bayesian Vector Autoregression
Authors@R: c(person("Nikolas",  "Kuschnig", role = c("aut", "cre"), email = "nikolas.kuschnig@wu.ac.at"),
person("Lukas", "Vashold", role = "aut"),
person("Michael", "McCracken", role = "dtc", comment = "author of the FRED-QD dataset"))
Michael McCracken [dtc] (author of the FRED-QD dataset)
Description: Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Allows for the computation of impulse responses and forecasts and provides functionality for assessing results.