Title: Wrapper functions for MAESTRA/MAESPA.
Description: A bundle of functions for modifying MAESTRA/MAESPA input
files, reading output files, and visualizing the stand in 3D.
Handy for running sensitivity analyses, scenario analyses, etc.

Title: Wrapper Functions for MAESTRA/MAESPA
Description: A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.

person("Pierre", "Latouche", , "pierre.latouche@gmail.com", role = "aut"),
person("Alexia", "Grenouilla", , "grenouil@insa-toulouse.fr", role = "ctb"),
person("Hugo", "Lelievre", , "lelievre@insa-toulouse.fr", role = "ctb")
Imports: broom, dplyr, ggplot2, magrittr, methods, mvtnorm, plyr,
purrr, Rcpp, rlang, stats, tibble, tidyr, tidyselect

Title: Magnetic Laplacian Matrix and Magnetic Eigenmap Visualization
Description: Constructs the normalized magnetic Laplacian Matrix of a square matrix, returns the eigenvectors and visualization of magnetic eigenmap.
Packaged: 2016-08-02 19:50:43 UTC; tianhaowu

person("Etienne", "Camenen", , "etienne.camenen@gmail.com", role = c("aut", "cre")),
person("Baptiste", "Couvy-Duchesne", role = "aut")
Description: Shiny for Open Science to visualize, share, and inventory the
main existing human datasets for researchers.
Imports: config, dplyr, DT, glue, golem, grDevices, htmltools,
htmlwidgets, magrittr, openxlsx, pkgload, processx, rlang, sf,
shiny, spData, tidyr, tmap, tmaptools, utils

Title: k-Means Algorithm with a Majorization-Minimization Method
Authors@R: c(person("Sheikhi", "Ayyub", email = "sheikhy.a@uk.ac.ir", role = c("aut", "cre")),
person("Yaghoubi", "Mohammad Ali", email = "yaghoobi@uk.ac.ir", role = "aut"))
Description: A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.