Author: Philipp Doebler with contributions from Bernardo Sousa-Pinto
Depends: R (>= 3.5.0), stats, mvtnorm, ellipse, mvmeta, metafor

approach based to diagnostic meta-analysis of Holling et al.
(2012) is also available. Standard methods like summary, plot

Description: This package provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.

Description: Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.

Description: Contains the Maddison Project database, which provides estimates of
GDP per capita for all countries in the world between AD 1 and 2010. See

Description: Contains the Maddison Project 2018 database, which provides
estimates of GDP per capita for all countries in the world between AD 1 and
2016. See <https://www.rug.nl/ggdc/historicaldevelopment/maddison/> for more information.

Title: 'MADGRAD' Method for Stochastic Optimization
person(family = "RStudio", role = c("cph")),
person(family = "MADGRAD original implementation authors.", role = c("cph"))
Description: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the
generalization performance of stochastic gradient descent and at least as fast
convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation
is provided based on Defazio et al (2020) <arxiv:2101.11075>.
MADGRAD original implementation authors. [cph]

Title: Pipe-Style Interface for 'data.table'
Authors@R: person("Gregory", "Demin", email = "gdemin@gmail.com", role = c("aut", "cre"))
Imports: data.table (>= 1.10), magrittr (>= 1.5)
Description: Package provides pipe-style interface for 'data.table'. It preserves all
'data.table' features without significant impact on performance. 'let'
and 'take' functions are simplified interfaces for most common data manipulation
tasks. For example, you can write 'mtcars %>% take(mean(mpg), by = am)' for
aggregation or 'mtcars %>% let(hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification.
Use 'take_if/let_if' for conditional aggregation/modification. 'query_if' function
translates its arguments one-to-one to '[.data.table' method. Additionally there are
some conveniences such as automatic 'data.frame' conversion to 'data.table'.