Description Usage Format Source References Examples

The alliances dataset contains the international defense alliance network among 164 countries, covering the years 1981–2000. In addition to the yearly defense alliance network, it contains data on military capabilities, governing regime type, geographic contiguity and international conflict. This is an excerpt from a dataset that has been used in two published analyses. The full dataset (Cranmer, Desmarais and Menninga 2012; Cranmer, Desmarais and Kirlkand 2012) contains a large number of countries and a much longer time series.

1 | ```
data("alliances")
``` |

`allyNet`

is a list of network objects at 20 time points, 1981–2000, containing undirected defense alliance networks. In addition to the alliance ties, each network object contains three vertex attributes.

`cinc`

is the "CINC" or Composite Index of National Capability score (see https://correlatesofwar.org/data-sets/national-material-capabilities).`polity`

is the "polity score" of each country in the respective year. Quoting the online description, "the Polity Score captures this regime authority spectrum on a 21-point scale ranging from -10 (hereditary monarchy) to +10 (consolidated democracy)," (see http://www.systemicpeace.org/polityproject.html).`year`

is simply the year recorded as a vertex attribute.`contigMat`

is a 164 x 164 binary matrix in which a 1 indicates that two countries share a border.

`lNet`

is a list of 20 matrices. Each element is the adjacency matrix from the previous year. This is used to model memory in the ties.

`LSP`

is a list of 20 matrices. Each element is a matrix recording the number of shared partners between countries in the alliance network from the previous year.

`warNet`

is a list of 20 matrices. Each element is a binary matrix that indicates whether two states were in a militarized interstate dispute in the respective year.

The data were gathered by Skyler Cranmer and Bruce Desmarais in the process of writing Cranmer, Desmarais and Menninga (2012) and Cranmer, Desmarais and Kirlkand (2012).

Permission to redistribute this dataset along with this package was granted by Skyler Cranmer and Bruce Desmarais on December 15, 2015. Questions about the data should be directed to them.

Skyler J. Cranmer, Bruce A. Desmarais, and Justin H. Kirkland (2012): Toward a Network Theory of Alliance Formation. *International Ineractions* 38(3): 295–324.

Skyler J. Cranmer, Bruce A. Desmarais, and Elizabeth Menninga (2012): Complex Dependencies in the Alliance Network. *International Ineractions* 29(3):279–313.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
## Not run:
library("xergm")
data("alliances")
# btergm formulas look very similar to ERGM formulas.
# Note the R argument; usually want R > 1000.
# Here it is set to 50 to limit computation time.
# First, set the seed for replicability.
set.seed(123)
model <- btergm(allyNet ~ edges + gwesp(0, fixed = TRUE)
+ edgecov(lNet) + edgecov(LSP) + edgecov(warNet)
+ nodecov("polity") + nodecov("cinc") + absdiff("polity")
+ absdiff("cinc") + edgecov(contigMat) + nodecov("year"),
R = 50)
# View estimates and confidence intervals.
summary(model)
# Evaluate model fit. Simulate 100 networks for each time point.
# Calculate edgewise shared partners, degree and geodesic distance
# distance distributions.
alliance_gof <- gof(model, statistics = c(deg, esp, geodesic))
# Plot goodness of fit.
plot(alliance_gof)
## End(Not run)
``` |

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