Changes in version 0.1.0 (2026-07-15)                  

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 (the RSiena model family). 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. Sufficient statistics (ergm_stats()), a TNT sampler
    (ergm_simulate()), pseudo-likelihood (ergm_mple()), and MCMC maximum
    likelihood (ergm_mcmle()), matching ergm::ergm() on benchmark
    models.
  - Temporal ERGM. tergm_mple() (pooled MPLE with block bootstrap,
    matching btergm), temporal simulation (tergm_simulate()), and the
    separable stergm_cmle() (formation/persistence, matching tergm
    CMLE).
  - ALAAM. alaam_mple() (exactly reproducing the corresponding glm),
    alaam_mcmle(), and a Gibbs simulator (alaam_simulate()).
  - Low-level host statistics (cusna_network_stats(),
    cusna_behavior_stats(), cusna_gof_distribution()) reproduce RSiena
    targets on public datasets to machine precision.