Depends: network (>= 1.9), statnet.common
Packaged: 2014-01-13 21:57:17 UTC; rpackagebuilder

Packaged: 2014-01-18 12:48:53 UTC; rpackagebuilder

Packaged: 2014-02-06 18:43:53 UTC; rpackagebuilder

Packaged: 2014-02-27 19:50:04 UTC; rpackagebuilder

Packaged: 2014-12-04 20:08:12 UTC; rpackagebuilder

Packaged: 2015-09-15 12:28:52 UTC; rpackagebuilder

Packaged: 2015-10-07 09:50:55 UTC; rpackagebuilder

Packaged: 2016-01-14 12:14:39 UTC; rpackagebuilder

Depends: R (>= 3.0),networkDynamic (>= 0.6), network (>= 1.9)
Description: A collection of dynamic network datasets from various sources and multiple authors represented networkDynamic format
Packaged: 2014-02-06 22:37:28 UTC; rpackagebuilder

Title: Dynamic (Longitudinal) Network Datasets
Description: A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Packaged: 2016-01-11 20:38:54 UTC; rpackagebuilder

"bsl28@cam.ac.uk", role = c("aut", "cre", "cph")))
Description: A network Maze generator that creates different types of network mazes.
Author: Bao Sheng Loe (Aiden) [aut, cre, cph]

Authors@R: c(person(given="Claus Thorn", family="Ekstrøm", email="ekstrom@sund.ku.dk", role=c("aut", "cre")),
Description: Collection of functions for fast manipulation, handling, and analysis of large-scale
networks based on family and social data. Functions are utility functions used to manipulate data
in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data.
When possible, the functions should be able to handle millions of data points quickly for use in combination
with data from large public national registers and databases.

Imports: Rcpp (>= 0.12.12), data.table, fastmatch, Matrix

Package: networkTomography
Type: Package
Title: Tools for network tomography
Version: 0.2
Author: Alexander W Blocker, Paul Koullick, Edoardo Airoldi
Maintainer: Alexander W Blocker <ablocker@gmail.com>
Description: networkTomography implements the methods developed and
evaluated in Blocker and Airoldi (2011) and Airoldi and Blocker
(2012). These include the authors' own dynamic multilevel model
with calibration based upon a Gaussian state-space model in
addition to implementations of the methods of Tebaldi & West
(1998; Poisson-Gamma model with MCMC sampling), Zhang et al.
(2002; tomogravity), Cao et al. (2000; Gaussian model with
mean-variance relation), and Vardi (1996; method of moments).
Data from the 1router network of Cao et al. (2000), the Abilene
network of Fang et al. (2007), and the CMU network of Blocker
and Airoldi (2011) are included for testing and
reproducibility.
License: LGPL-2
LazyLoad: yes
URL: https://github.com/awblocker/networkTomography
Depends: R (>= 2.10.0), Rglpk (>= 0.3), coda (>= 0.11-3), MASS, KFAS
(>= 0.9), limSolve (>= 1.4), igraph (>= 0.5), stringr, plyr
Collate: 'caoEtAl.R' 'ipfp.R' 'lib.R' 'ssmMCMC.R' 'ssmMle.R' 'twMCMC.R'
'vardi.algorithm.R' 'data.R' 'parseTopology.R' 'gravity.R'
'tomogravity.R'
Packaged: 2012-12-13 05:44:56 UTC; awblocker
Repository: CRAN
Date/Publication: 2012-12-13 06:49:18