First release: a self-contained native engine (C ABI over CUDA kernels and C++ host logic) for the simulation and estimation of network models, callable from R without a Python runtime. The CRAN build is CPU-only from source; the GPU path compiles when a CUDA toolkit is detected at configure time.
saom_data(); effect constructors
(cusna_effect(), cusna_beh_effect(),
cusna_rate_effect(), cusna_interaction()); the
full Method-of-Moments estimator mom_estimate() /
mom_control() returning a cusna_fit object
with summary(), coef(), vcov(),
and as.data.frame() methods; behavior co-evolution,
composition change, conditional and unconditional estimation;
multi-network co-evolution (saom_multinet_data(),
mom_estimate_multinet()); and a siena07()
simulation backend (cusna_fran()). Data preparation, effect
preprocessing, and moment targets are validated bit-for-bit against the
reference implementation; estimates agree within simulation standard
errors.ergm_stats()), a TNT sampler
(ergm_simulate()), pseudo-likelihood
(ergm_mple()), and MCMC maximum likelihood
(ergm_mcmle()), matching ergm::ergm() on
benchmark models.tergm_mple() (pooled
MPLE with block bootstrap, matching btergm), temporal
simulation (tergm_simulate()), and the separable
stergm_cmle() (formation/persistence, matching
tergm CMLE).alaam_mple() (exactly
reproducing the corresponding glm),
alaam_mcmle(), and a Gibbs simulator
(alaam_simulate()).cusna_network_stats(),
cusna_behavior_stats(),
cusna_gof_distribution()) reproduce RSiena targets on
public datasets to machine precision.