Title: Tools for Principal Component Analysis-Based Data Structure
Authors@R: c(person("Anne H.", "Petersen", , email="ahpe@sund.ku.dk", role=c("aut", "cre")),
person("Bo", "Markussen", , email="bomar@math.ku.dk", role=c("aut")))
Description: A suite of non-parametric, visual tools for assessing differences in data structures
for two datasets that contain different observations of the same variables. These tools are all
based on Principal Component Analysis (PCA) and thus effectively address differences in the structures
of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use,
intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue
(CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The
angle plot presents the information loss when moving from the PCA decomposition of one dataset to the
PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby
presenting the relative weighting and importance of the variables from the original dataset.
Imports: reshape2, methods, pander, ggplot2, Matrix
Maintainer: Anne H. Petersen <ahpe@sund.ku.dk>

Title: Match Cases to Controls Based on Genotype Principal Components
Maintainer: Derek W. Brown <derek.brown@nih.gov>
Description: Matches cases to controls based on genotype principle components (PC).
In order to produce better results, matches are based on the weighted
distance of PCs where the weights are equal to the % variance explained
by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)

Description: Matches cases to controls based on genotype principal components (PC).
Suggests: optmatch, testthat, knitr, rmarkdown

Suggests: optmatch, testthat, knitr, rmarkdown, R.rsp
Author: Derek W. Brown [aut, cre] (<https://orcid.org/0000-0001-8393-1713>),
Mitchel J. Machiela [aut] (<https://orcid.org/0000-0001-6538-9705>),

Maintainer: Derek W. Brown <derek9@gwmail.gwu.edu>