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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.