Title: An Analysis Toolbox for Hermitian Positive Definite Matrices
Description: An implementation of data analysis tools for samples of symmetric or
Hermitian positive definite matrices, such as collections of covariance matrices
or spectral density matrices. The tools in this package can be used to perform (i)
manifold wavelet regression and clustering for curves of Hermitian positive definite
matrices, and (ii) exploratory data analysis and inference for samples of (curves of)
Hermitian positive definite matrices by means of manifold data depth and manifold
Imports: astsa, multitaper, Rcpp, ddalpha, shapes

Imports: astsa, multitaper, Rcpp, ddalpha

intrinsic manifold wavelet regression and clustering for curves (1D) or surfaces (2D)
of Hermitian positive definite matrices, and (ii) exploratory data analysis and inference
for samples of (curves of) Hermitian positive definite matrices by means of intrinsic manifold
data depth and manifold rank-based hypothesis tests.
Imports: grid, multitaper, Rcpp, ddalpha, R.utils
Suggests: knitr, rmarkdown, testthat, ggplot2, reshape2, viridis,
Packaged: 2017-12-09 09:14:51 UTC; jorischau

Suggests: knitr, rmarkdown, testthat, grid, ggplot2, reshape2, viridis,
Packaged: 2017-12-10 14:55:20 UTC; jorischau

or spectral density matrices. The tools in this package can be used to perform: (i)
intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive
definite matrices with applications to dimension reduction, denoising and clustering in the
space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference
for samples of positive definite matrices by means of intrinsic data depth functions and
rank-based hypothesis tests in the space of Hermitian positive definite matrices.
Imports: multitaper, Rcpp, ddalpha, Rdpack

Authors@R: c(person("Chongliang", "Luo", role=c("aut","cre"), email ="luocl3009@gmail.com"),
person("Rui", "Duan", role="aut"), person("Mackenzie", "Edmondson", role="aut"),
person("Yong", "Chen", role="aut", email ="ychen123@upenn.edu"),
person("Penn Computing Inference Learning (PennCIL) lab", role = c("cph")))
Description: A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, or Poisson regression for count outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
Imports: Rcpp (>= 0.12.19), stats, httr, rvest, jsonlite, data.table,
Penn Computing Inference Learning (PennCIL) lab [cph]