c(person("Joris","Meys", email = "Joris.Meys@UGent.be", role = c('aut', 'cre')),
person("Jan","De Neve", email = "Jan.DeNeve@UGent.be", role = 'aut'),
person("Nick","Sabbe", role = 'aut'),
person("Gustavo", "Guimaraes de Castro Amorim", role = 'aut'))
Joris Meys [aut, cre], Jan De Neve [aut], Nick Sabbe [aut], Gustavo Guimaraes de Castro Amorim [aut]

Joris Meys [aut, cre],
Jan De Neve [aut],
Nick Sabbe [aut],
Gustavo Guimaraes de Castro Amorim [aut]
Fit a probabilistic index model as described in
Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the
modeling function has changed in this new version. The old version is
still available at R-Forge.

Prediction Intervals for Random-Effects Meta-Analysis
c(
person("Kengo", "Nagashima", email = "nshi1201@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4529-9045")),
person("Hisashi", "Noma", role = c("aut")),
person("Toshi A.", "Furukawa", role = c("aut")))
An implementation of prediction intervals for random-effects meta-analysis:
Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
<doi:10.1002/sim.7140>, and Nagashima et al. (2018) <arXiv:1804.01054>.
Kengo Nagashima [aut, cre] (<https://orcid.org/0000-0003-4529-9045>),
Hisashi Noma [aut],
Toshi A. Furukawa [aut]

An implementation of prediction intervals for random-effects meta-analysis:
Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
<doi:10.1002/sim.7140>, and Nagashima et al. (2018) <doi:10.1177/0962280218773520>,
<arXiv:1804.01054>.

An implementation of prediction intervals for random-effects meta-analysis:
Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
<doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>,
<arXiv:1804.01054>.

Fast and Stable Estimation of the Probability of Informed
Trading (PIN)
person("Andreas", "Recktenwald", email = "a.recktenwald@mx.uni-saarland.de",
role = c("aut", "cre"))
Utilities for fast and stable estimation of the probability of
informed trading (PIN) in the model introduced by Easley et al. (2002)
<DOI:10.1111/1540-6261.00493> are implemented. Since the basic model developed
by Easley et al. (1996) <DOI:10.1111/j.1540-6261.1996.tb04074.x> is nested in the
former due to equating the intensity of uninformed buys and sells, functionalities
can also be applied to this simpler model structure, if needed.
stats, fastcluster, lubridate, ggplot2, reshape2, scales