Youyi Fong <youyifong@gmail.com>, Saheli Datta, Krisztian Sebestyen
Testing methods for kernel-based regression models.

Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence.

c(person("Youyi", "Fong", role = "cre", email = "youyifong@gmail.com"), person("Saheli", "Datta", role = "aut", email = "saheli8@gmail.com"), person("Krisztian", "Sebestyen", role = "aut", email = "ksebestyen@gmail.com"), person("Steve", "Park", role = "ctb"), person("Dave", "Geyer", role = "ctb"))
Youyi Fong [cre],
Saheli Datta [aut],
Krisztian Sebestyen [aut],
Steve Park [ctb],
Dave Geyer [ctb]

Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.

grDevices, graphics, multicool, stats, utils
Kernel smoothers for univariate and multivariate data.

FNN, KernSmooth (>= 2.22), misc3d (>= 0.4-0), multicool, rgl
(>= 0.66)
Kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, and two-sample hypothesis testing.

Kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, significant modal regions and two-sample hypothesis testing.

FNN (>= 1.1), kernlab, KernSmooth (>= 2.22), Matrix, mclust,
mgcv, misc3d (>= 0.4-0), multicool, mvtnorm (>= 1.0-0),
OceanView, rgl (>= 0.66)
Kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, classification (discriminant analysis), significant modal regions and two-sample hypothesis testing.