Imports: grDevices, grid, gtable (>= 0.2.0), plyr (>= 1.8.3), progress,
RColorBrewer, reshape (>= 0.8.5), utils, rlang

person("Joseph", "Larmarange", role = "aut", email = "joseph@larmarange.net", comment = "ggcoef"),
person("Ott", "Toomet", role = "ctb", email = "otoomet@gmail.com"))

person("Barret", "Schloerke", role = c("aut", "cre"), email = "schloerke@gmail.com"),
person("Di", "Cook", role = c("aut", "ths"), email = "dicook@monash.edu"),
person("Joseph", "Larmarange", role = "aut", email = "joseph@larmarange.net"),
person("Francois", "Briatte", role = "aut", email = "f.briatte@gmail.com"),
person("Moritz", "Marbach", role = "aut", email = "mmarbach@mail.uni-mannheim.de"),
person("Edwin", "Thoen", role = "aut", email = "edwinthoen@gmail.com"),
person("Amos", "Elberg", role = "aut", email = "amos.elberg@gmail.com"),
person("Ott", "Toomet", role = "ctb", email = "otoomet@gmail.com"),
person("Jason", "Crowley", role = "aut", email = "crowley.jason.s@gmail.com"),
person("Hadley", "Wickham", role = "ths", email = "h.wickham@gmail.com")
RColorBrewer, reshape (>= 0.8.5), scales (>= 1.1.0), utils,
Suggests: broom (>= 0.4.0), chemometrics, geosphere (>= 1.5-1),
ggforce, Hmisc, igraph (>= 1.0.1), intergraph (>= 2.0-2), maps
(>= 3.1.0), mapproj, network (>= 1.12.0), scagnostics, sna (>=
2.3-2), survival, rmarkdown, roxygen2, testthat, crosstalk,

Imports: dplyr (>= 1.0.0), forcats, grDevices, grid, gtable (>= 0.2.0),
lifecycle, plyr (>= 1.8.3), progress, RColorBrewer, reshape (>=
0.8.5), rlang, scales (>= 1.1.0), tidyr, utils
Suggests: broom (>= 0.7.0), broom.helpers (>= 1.1.0), chemometrics,
geosphere (>= 1.5-1), ggforce, Hmisc, igraph (>= 1.0.1),
intergraph (>= 2.0-2), labelled, maps (>= 3.1.0), mapproj,
nnet, network (>= 1.12.0), scagnostics, sna (>= 2.3-2),
survival, rmarkdown, roxygen2, testthat, crosstalk, knitr,

nnet, network (>= 1.17.1), scagnostics, sna (>= 2.3-2),

Title: Gauss Hermite Quadrature with pruning.
Maintainer: Freddy Hernandez Barajas <fhernanb@gmail.com>
Description: The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.