Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical
Models in S" (Chambers and Hastie (eds), 1991), and
"Generalized Additive Models" (Hastie and Tibshirani, 1990).

Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

Generalized Additive Models for Bivariate Conditional Dependence
Structures and Vine Copulas
c(
person("Thomas", "Nagler",, "thomas.nagler@tum.de", role = "aut"),
person("Thibault", "Vatter",, "thibault.vatter@gmail.com", role = c("aut", "cre"))
)
graphics, stats, utils, VineCopula (>= 2.0.0), mgcv, MASS,
gsl, numDeriv, methods, copula, igraph (>= 1.0.0), parallel,
foreach, doParallel
Implementation of various inference and simulation tools to
apply generalized additive models to bivariate dependence structures and
non-simplified vine copulas.
Thomas Nagler [aut],
Thibault Vatter [aut, cre]

To calculate the relative risk (RR) for the generalized additive model.

Zhicheng Du, Wangjian Zhang, Yuantao Hao

Genetic Approach to Maximize Clustering Criterion
c(person(given = "Jairson", family ="Rodrigues", role = c("aut", "cre"),
email = "jairson.rodrigues@univasf.edu.br", comment = c(ORCID = "0000-0003-1176-3903")),
person(given = "Germano", family ="Vasconcelos", role = c("aut", "ths"),
email = "gcv@cin.ufpe.br", comment = c(ORCID = "0000-0002-1899-1506")),
person(given = "Renato", family = "Tin\'{o}s", role = c("aut", "rev"),
email = "rtinos@ffclrp.usp.br", comment = c(ORCID = "0000-0003-4027-8851")))
An evolutionary approach to performing hard partitional clustering. The algorithm uses genetic operators guided by information about the quality of individual partitions. The method looks for the best barycenters/centroids configuration (encoded as real-value) to maximize or minimize one of the given clustering validation criteria: Silhouette, Dunn Index, C-Index or Calinski-Harabasz Index. As many other clustering algorithms, 'gama' asks for k: a fixed a priori established number of partitions. If the user does not know the best value for k, the algorithm estimates it by using one of two user-specified options: minimum or broad. The first method uses an approximation of the second derivative of a set of points to automatically detect the maximum curvature (the 'elbow') in the within-cluster sum of squares error (WCSSE) graph. The second method estimates the best k value through majority voting of 24 indices. One of the major advantages of 'gama' is to introduce a bias to detect partitions which attend a particular criterion. References: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04>; CHARRAD, Malika et al. (2014) <doi:10.18637/jss.v061.i06>; Tsagris M, Papadakis M. (2018) <doi:10.7287/peerj.preprints.26605v1>; Kaufman, L., & Rousseeuw, P. (1990, ISBN:0-47 1-73578-7).
ArgumentCheck, cluster, clusterCrit, NbClust, GA, ggplot2,
methods, Rfast
Jairson Rodrigues [aut, cre] (<https://orcid.org/0000-0003-1176-3903>),
Germano Vasconcelos [aut, ths]
(<https://orcid.org/0000-0002-1899-1506>),
Renato Tin'{o}s [aut, rev] (<https://orcid.org/0000-0003-4027-8851>)