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I've read some papers about p* and one paper about p2.

If I've read correctly, Equation 4 of reference 2 ;extends ;first p* 
(equation 1) to contain covariates. And p2 is 'the extension of p1 that 
doesn't assume dyadic independence and accounts for nodal & dyadic 
covariates.'

Does p* (equation 4 in reference 2) contain p2? Because p* has p1 as 
submodels, p2's model equation is very similar to p*'s. If not, what is the 
unique features of p2?

Thanks in advance for any comments.

--- Equations ---
Equation 1 of Reference 2 ; : ; P(X = x) ;= exp(theta' * ;t(x)) / c(theta)
Equation 4 of Reference 2 ; : ; P(X = x) = exp(vec(x)' Z beta + sum of 
alpha_k * d_k(x) + theta' * t(x)) / c(alpha, beta, theta) ; (d_k : the 
number of individuals with exactly k links, Z : exogenous dyadic 
covariates)


--- References ---
1. Logit models and logistic regressions for social networks : I An 
Introduction to markov graphs and p*. Stanley ;Wasserman...
2. statnet: An R package for the Statistical Analysis and Simulation of 
Social Networks, Mark S. Handcock...
3. p2: a random effects model with covariates for directed graphs. Marijtje 
A. J. van Duijn...


Best Regards
Se Kwon, Kim

-------------
KAIST (Korean Advanced Institute of Science & Technology)
Mathematics Student
http://math.kaist.ac.kr

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