Inference of Species Associations from Co-occurrence Data
Infers species associations from local and regional-scale co-occurrence data (species x site matrices)

Inference of Species Associations from Co-Occurrence Data
Infers species associations from local and regional-scale co-occurrence data (species x site matrices).

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.

Inferring Causal Effects on Collective Outcomes under
Interference
c(person("Elizabeth", "Ogburn", role="aut",
email="eogburn@jhu.edu"),
person("Ilya", "Shpitser", role = "aut",
email = "ilyas@cs.jhu.edu"),
person("Youjin", "Lee", role = c("aut", "cre"),
email = "ylee160@jhu.edu"))
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>.
Rcpp (>= 0.12.17), Matrix, gtools, stringr, stats, igraph
Elizabeth Ogburn [aut],
Ilya Shpitser [aut],
Youjin Lee [aut, cre]

c(person("Elizabeth", "Ogburn", role="aut",
email="eogburn@jhu.edu"),
person("Ilya", "Shpitser", role = "aut",
email = "ilyas@cs.jhu.edu"),
person("Youjin", "Lee", role = c("aut", "cre"),
email = "youjin.lee@pennmedicine.upenn.edu"))

Spatio-Network Generalised Linear Mixed Models for Areal Unit
and Network Data
George Gerogiannis, Mark Tranmer, Duncan Lee
Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).
Rcpp (>= 1.0.4), coda, ggplot2, mvtnorm, MASS