Description: Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Imports: stats, MASS, abind, boot, energy, raster
Suggests: testthat (>= 2.1.0), ggplot2, reshape2, knitr, rmarkdown

Title: Visualisations for Generalized Additive Models
Authors@R: c(person("Matteo", "Fasiolo", email = "matteo.fasiolo@gmail.com", role = c("aut", "cre")),
person("Raphael", "Nedellec", email = "raphael.nedellec@edf.fr", role = c("aut")))
Description: Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models (GAMs) that exploit the additive structure of GAMs, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Imports: shiny, matrixStats, viridis, colourpicker, miniUI, rstudioapi,
plotly, GGally, KernSmooth, data.table, dplyr, gridExtra,

person("Raphael", "Nedellec", email = "raphael.nedellec@edf.fr", role = c("aut")),
person("Yannig", "Goude", role = c("ctb")), person("Simon", "N. Wood", role = c("ctb")))
Description: Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Imports: matrixStats, viridis, GGally, KernSmooth, gridExtra, plyr,