Implements methods for clustering mixed-type data,
specifically combinations of continuous and nominal data. Special attention
is paid to the often-overlooked problem of equitably balancing the
contribution of the continuous and categorical variables. This package
implements KAMILA clustering, a novel method for clustering
mixed-type data in the spirit of k-means clustering. It does not require
dummy coding of variables, and is efficient enough to scale to rather large
data sets. Also implemented is Modha-Spangler clustering, which uses a
brute-force strategy to maximize the cluster separation simultaneously in the
continuous and categorical variables. For more information, see Foss, Markatou,
Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou
(2018) <doi:10.18637/jss.v083.i13>.
stats, abind, KernSmooth, gtools, Rcpp, plyr

Kernel Approaches for Nonlinear Genetic Association Regression
Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut],
Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb],
Heike Bickeboeller [ctb]
Methods to extract information on pathways, genes and SNPs from
online databases. It provides functions for data preparation and evaluation
of genetic influence on a binary outcome using the logistic kernel machine
test (LKMT). Three different kernel functions are offered to analyze genotype
information in this variance component test: A linear kernel, a size-adjusted
kernel and a network based kernel.
'pathway.r' 'GWASdata.r' 'data.R' 'kernel.r' 'lkmt.r'
methods, KEGGgraph, biomaRt, bigmemory, sqldf, CompQuadForm,
data.table, lattice, igraph

Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel (Friedrichs et al., 2017, <doi:10.1155/2017/6742763>).
methods, biomaRt, bigmemory, sqldf, CompQuadForm, data.table,
lattice, igraph