Packaged: 2022-09-21 16:52:56 UTC; alex.hubbard

person("Alexander", "Foss", email = "alexanderhfoss@gmail.com",
person("Marianthi", "Markatou", role = c("aut"))
Title: Methods for Clustering Mixed-Type Data
Description: 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
Imports: stats, abind, KernSmooth, gtools, Rcpp, mclust, plyr
Suggests: testthat, clustMD, ggplot2, Hmisc
Packaged: 2016-08-18 17:46:08 UTC; Alexander

continuous and categorical variables. For more information, see Foss, Markatou,
Imports: stats, abind, KernSmooth, gtools, Rcpp, plyr

Title: Kernel Approaches for Nonlinear Genetic Association Regression
Author: Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut],
Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb],
Description: 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
Collate: 'pathway.r' 'GWASdata.r' 'data.R' 'kernel.r' 'lkmt.r'
Imports: methods, KEGGgraph, biomaRt, bigmemory, sqldf, CompQuadForm,

Description: 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>).
Imports: methods, biomaRt, bigmemory, sqldf, CompQuadForm, data.table,