Author: Josmar Mazucheli, Larissa B. Fernandes and Ricardo P. de Oliveira
Description: Computes  the  probability density,  the  cumulative  distribution, the quantile and
the hazard rate functions and generates random deviates from the discrete and continuous
Lindley  distribution  as  well  as  for 19 of  its  modifications. It  also  generates censored
random deviates from any probability  distribution available in R.

Title: Change Point Detection in High-Dimensional Linear Regression
Authors@R: c(person("Yue", "Bai", email = "baiyue@ufl.edu", role = c("aut", "cre")),
person("Abolfazl", "Safikhani", email = "a.safikhani@ufl.edu", role = c("aut")))
Description: A unified framework for simultaneous structural break detection and parameter estimation in high-dimensional linear models. The proposed method can handle a wide range of models, including change-in-mean model, multiple linear regression model, Vector auto-regressive model and Gaussian graphical model.
Imports: stats, graphics, mvtnorm, factoextra, Rcpp, lattice, xtable,

Imports: stats, graphics, mvtnorm, factoextra, Rcpp, ggplot2, glmnet,

Title: Minimum Distance Estimation in Linear Regression Model
Description: Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.

Title: Linearized Support Vector Regression
Author: Sriharsha Veeramachaneni and Ken Williams
Description: Train and predict using fast prototype-based Linearized
Depends: kernlab, LiblineaR, expectreg, testthat
Packaged: 2012-03-08 20:48:55 UTC; kwilliams

Packaged: 2014-02-26 17:35:16 UTC; kwilliams

Packaged: 2014-08-01 14:50:38 UTC; kwilliams

Title: Clustering Communication Networks Using the Stochastic Topic
Imports: httr, jsonlite, RColorBrewer, sna, network
Author: Charles Bouveyron, Pierre Latouche, Stéphane Petiot, Carlos Ocanto
Description: It allows to cluster communication networks using the Stochastic
Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through
the API of the linkage.fr server, which implements the clustering method.
The package also allows to visualize the clustering results returned by the server.