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

Samantha F. Anderson <sander10@nd.edu>, Ken Kelley <kkelley@nd.edu>
Implements a method of correcting for publication bias and
uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (submitted, revised and resubmitted).

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

Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and
uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

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"))
Nikolas Kuschnig [aut, cre],
Lukas Vashold [aut],
Michael McCracken [dtc] (author of the FRED-QD dataset)
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.

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"))
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 several methods for assessing results.