Description: A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis <https://link.springer.com/article/10.1007/BF02289447>). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.

Title: Flexible Non-Linear Regression with the HorseRule Algorithm
Author: Malte Nalenz <malte.nlz@googlemail.com>, Mattias Villani <mattias.villani@liu.se>
Description: Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression and classification described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Imports: mvnfast, MASS, randomForest, gbm, inTrees, ggplot2,

Description: Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Imports: mvnfast, MASS, randomForest, gbm, inTrees, ggplot2, grDevices,

Package: horseshoe
Title: Implementation of the Horseshoe Prior
Version: 0.1.0
Authors@R: c(
person("Stephanie", "van der Pas", email = "svdpas@math.leidenuniv.nl", role = c("cre", "aut")),
person("James", "Scott", email = "James.Scott@mccombs.utexas.edu", role = "aut"),
person("Antik", "Chakraborty", email = "antik@stat.tamu.edu", role = "aut"),
person("Anirban", "Bhattacharya", email = "anirbanb@stat.tamu.edu", role = "aut"))
Description: Contains functions for applying the horseshoe prior to high-
dimensional linear regression, yielding the posterior mean and credible
intervals, amongst other things. The key parameter tau can be equipped with
a prior or estimated via maximum marginal likelihood estimation (MMLE).
The main function, horseshoe, is for linear regression. In addition, there
are functions specifically for the sparse normal means problem, allowing
for faster computation of for example the posterior mean and posterior
variance. Finally, there is a function available to perform variable
selection, using either a form of thresholding, or credible intervals.
Depends: R (>= 3.1.0)
Imports: stats
Suggests: Hmisc
Encoding: UTF-8
License: GPL-3
LazyData: false
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-11-08 15:53:54 UTC; stephanie
Author: Stephanie van der Pas [cre, aut],
James Scott [aut],
Antik Chakraborty [aut],
Anirban Bhattacharya [aut]
Maintainer: Stephanie van der Pas <svdpas@math.leidenuniv.nl>
Repository: CRAN
Date/Publication: 2016-11-08 18:36:01