Title: A multi-platform GUI for drawing customizable graphs in R
Description: GrapheR is a multi-platform user interface for drawing

Description: GrapheR is a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.

Title: A Multi-Platform GUI for Drawing Customizable Graphs in R
Description: A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.

Author: Kofi Placid Adragni and Seongho Wu
Maintainer: Kofi Placid Adragni <kofi.adragni@gmail.com>
Description: Optimizing a function F(U), where U is a semi-orthogonal
matrix and F is invariant under an orthogonal transformation of

Maintainer: Kofi Placid Adragni <kofi@umbc.edu>

Description: Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U.

Description: Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U

Authors@R: person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardo.rastelli@wu.ac.at")
Description: Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) "Optimal Bayesian estimators for latent variable cluster models" <arXiv:1607.02325>.
Packaged: 2017-08-02 10:09:09 UTC; rrastelli

Authors@R: person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardoras@gmail.com")
Description: Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.