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cusna (native) — GPU-accelerated SAOM/RSiena, ERGM, and friends for R

cusna is a self-contained native engine — a C ABI over CUDA kernels and C++ host logic (libcusna) — callable from R without a Python runtime. It is the native counterpart of the reticulate-based cusna R wrapper, modelled on the R torch package:

the CRAN build is CPU-only from source (pure C++, needs only a C++17 compiler);
the GPU path is compiled from source when a CUDA 12.x toolkit (nvcc) is detected at configure time.

cusna_has_cuda() reports which build you have.

What the package provides

Family	Functions	Validated against
SAOM (RSiena)	`saom_data()`, `cusna_effect()`, `mom_estimate()`, `cusna_fit` methods; behavior co-evolution, composition change, `mom_estimate_multinet()`, `cusna_fran()`	data/masks/targets bit-identical to the reference; estimates within simulation SE; RSiena targets to machine zero
ERGM	`ergm_simulate()` (TNT sampler), `ergm_stats()`, `ergm_mple()`, `ergm_mcmle()`	sampler ≡ `ergm::simulate`; MLE matches `ergm::ergm()`
Temporal ERGM	`tergm_mple()` (+ block bootstrap), `tergm_simulate()`, `stergm_cmle()`	matches `btergm` to machine precision; `tergm` CMLE within SE
ALAAM	`alaam_mple()`, `alaam_mcmle()`, `alaam_simulate()`	MPLE ≡ `glm`; MLE recovers observed moments
Low-level	`cusna_network_stats()`, `cusna_behavior_stats()`, `cusna_gof_distribution()`	RSiena Appendix B conventions, machine zero

The underlying C ABI is bit-for-bit validated in native/test (see native/VALIDATION.md).

Reviewer quickstart (CPU-only, no GPU needed)

# from a checkout of the monorepo (configure vendors ../../native sources):
install.packages("cpp11")            # build-time only
# then, with a C++17 toolchain (Rtools on Windows):
#   R CMD INSTALL Rpkg-native
library(cusna)
cusna_has_cuda()                     # FALSE on the CPU-only build

# a two-wave panel and a Method-of-Moments SAOM fit, all native:
set.seed(7)
w1 <- matrix(as.integer(runif(400) < 0.12), 20, 20); diag(w1) <- 0L
w2 <- w1; flip <- sample(400, 40); w2[flip] <- 1L - w2[flip]; diag(w2) <- 0L
fit <- mom_estimate(saom_data(list(w1, w2)),
                    effects = list(cusna_effect("density"), cusna_effect("recip")))
summary(fit)

# an ERGM maximum-likelihood fit on the same data:
ergm_mcmle(w1, list(ergm_term("edges"), ergm_term("mutual")), directed = TRUE)

See vignette("cusna") for the full tour (covariates, co-evolution, multi-network models, TERGM/STERGM/ALAAM) and vignette("siena07-backend") for driving RSiena’s siena07() on the native simulator.

Building

See BUILD.md. In short: the CPU-only build needs a C++17 compiler (Rtools on Windows); the GPU build additionally needs a CUDA 12.x toolkit. The native sources are vendored from ../../native/ by configure.

License

MIT (see LICENSE). We compare outputs against RSiena but do not link its code.

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.