Title: Miscellaneous Functions for Network Analysis
person("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
Description: Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.

Suggests: covr, GA, testthat (>= 3.0.0), VGAM

Title: Inference of Species Associations from Co-occurrence Data
Author: Benjamin Blonder, Naia Morueta-Holme
Description: Infers species associations from local and regional-scale co-occurrence data (species x site matrices)
Packaged: 2015-01-30 22:38:14 UTC; benjaminblonder

Title: Inference of Species Associations from Co-Occurrence Data
Description: Infers species associations from local and regional-scale co-occurrence data (species x site matrices).
Packaged: 2015-03-13 17:01:47 UTC; benjaminblonder

Packaged: 2015-06-24 15:19:46 UTC; benjaminblonder

Description: Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
Imports: rags2ridges, corpcor, huge, vegan
Packaged: 2015-09-09 17:24:16 UTC; benjaminblonder

Packaged: 2015-11-23 21:40:00 UTC; benjaminblonder

Packaged: 2017-06-05 18:08:49 UTC; benjaminblonder

Packaged: 2022-07-20 08:37:07 UTC; benjaminblonder

Title: Inferring Causal Effects on Collective Outcomes under
person("Ilya", "Shpitser", role = "aut",
person("Youjin", "Lee", role = c("aut", "cre"),
Description: In networks, treatments may spill over from the treated individual to his or her social contacts and outcomes may be contagious over time. Under this setting, causal inference on the collective outcome observed over all network is often of interest. We use chain graph models approximating the projection of the full longitudinal data onto the observed data to identify the causal effect of the intervention on the whole outcome. Justification of such approximation is demonstrated in Ogburn et al. (2018) <arXiv:1812.04990>.
Imports: Rcpp (>= 0.12.17), Matrix, gtools, stringr, stats, igraph