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cusna_fran() now honours RSiena’s
simstats0c contract: fra and sc
are periods-by-parameters matrices (one row per period). In 0.1.0
fra was the period-summed vector and sc held
the last period only, so siena07() stopped at its first
call (in colSums(zz$fra)). The closure also checks that the
parameter vector passed by siena07() matches
effect_names and conditional, draws its seeds
from R’s random number stream (so
sienaAlgorithmCreate(seed = ) makes a run reproducible, and
the global seed is no longer reset on every call), and counts its calls
in attr(fran, "counter")$i.mom_estimate() can now return the simulated networks:
with return_nets = TRUE the fit keeps the phase-3 end
networks in fit$sims (one chains-by-n-by-n integer array
per period, the layout cusna_gof_distribution() takes) and,
for co-evolution, the end behavior in fit$sims_behavior. In
0.1.0 the simulator was always called without its end networks.s50 (three waves of friendship among 50
girls, with alcohol use; the RSiena teaching panel), used by the
examples and vignettes.\dontrun{} remains, and the SAOM
examples use s50. The siena07() vignette runs
its example when RSiena is installed.R CMD check --as-cran
(_R_CHECK_LIMIT_CORES_) the OpenMP engine uses at most two
threads.stergm_simulate(): draws STERGM transitions on the
constrained spaces of stergm_cmle().configure messages and
cusna_has_cuda() documentation now state that the R package
builds the CPU backend of the engine; the CUDA backend of the same C
interface is built separately.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.
Health stats visible at Monitor.