Title: Proximity Catch Digraphs and Their Applications
Description: Contains the functions for generating patterns of segregation, association, CSR (complete spatial randomness)
and Uniform data in one, two and three dimensional cases, for testing these patterns based on two invariants of
various families of the proximity catch digraphs (PCDs) (see (Ceyhan (2005) ISBN:978-3-639-19063-2).
The graph invariants used in testing spatial point data are the  domination number (Ceyhan (2011)
Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>) of for two-dimensional data  for visualization of PCDs for one,
two and three dimensional data. The PCD families considered are Arc-Slice PCDs,
Proportional-Edge PCDs and Central Similarity PCDs.
Imports: combinat, graphics, interp, plot3D, plotrix, Rdpack (>= 0.7),

The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>) of for two-dimensional data for visualization of PCDs for one,

Description: Contains the functions for generating patterns of segregation, association, complete spatial randomness (CSR))

Title: Model Fitting to Progressively Censored Mixture Data
Authors@R: c(person("Lida", "Fallah", role = c("aut", "cre"), email =
"l.fallah22@gmail.com"), person("John", "Hinde", role = "aut"))
Description: Functions for generating progressively Type-II censored data in
a mixture structure and fitting models using a constrained EM algorithm. It can also
create a progressive Type-II censored version of a given real dataset to be considered for
Packaged: 2017-07-24 17:18:11 UTC; LidaFallah

Title: Permutations and Combinations Enumeration
Description: Provide methods to iterate through all
permutations/combinations without retrieving all (avoid RAM

Title: Principal Component of Explained Variance
Description: Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate
response vector. It is a dimension-reduction technique, similar to Principal
component analysis (PCA), which seeks the maximize the proportion of
variance (in the response vector) being explained by a set of covariates.