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.