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://penncil2019pda.wixsite.com/mysite>.

Maintainer: Andreas M. Brandmaier <brandmaier@mpib-berlin.mpg.de>
Description: Permutation Distribution Clustering is a clustering method
for time series. Dissimilarity of time series is formalized as
the divergence between their permutation distributions. The
permutation distribution was proposed as measure of the

Description: Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.

Packaged: 2014-11-10 16:06:03 UTC; brandmaier

Packaged: 2014-11-21 12:39:54 UTC; brandmaier

Imports: stats, utils, grDevices, graphics
Packaged: 2015-09-28 12:31:31 UTC; brandmaier

Packaged: 2014-10-16 11:19:52 UTC; brandmaier

Authors@R: c(person(given = "Yves",      family = "Croissant",    role = c("aut", "cre"), email = "yves.croissant@univ-reunion.fr"),
person(given = "Giovanni",  family = "Millo",        role = "aut", email = "Giovanni.Millo@Generali.com"))
Description: Data sets for the Panel Data Econometrics with R book.

Suggests: car, dplyr, ggplot2, lmtest, msm, pglm, plm, splm, survival,
Description: Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.

Suggests: car, dplyr, ggplot2, lmtest, msm, pglm, splm, survival,

Title: Cluster analysis via nonparametric density estimation
Author: Adelchi Azzalini, Giovanna Menardi, Tiziana Rosolin
Description: The package performs cluster analysis via kernel density
estimation. Clusters are associated to the maximally connected
components with estimated density above a threshold.
Diagnostics methods for evaluating the quality of clustering
are also available.  The package includes also a routine to
estimate the probability density function by the kernel method,
given a set of data with arbitrary dimensions.