Accelerating siena07() with cusna

Besides the standalone estimator (vignette("cusna")), cusna can serve as an acceleration backend for RSiena: the native simulator replaces RSiena’s inner simulation loop, while RSiena keeps its full Robbins–Monro estimation machinery — and hence its convergence behavior. Nothing in RSiena is forked or patched.

This is a useful path for hard, nearly collinear specifications, where a standalone method-of-moments run may not converge from a cold start: RSiena supplies convergence, cusna supplies speed.

The chunks below run when RSiena is installed (it is a suggested package).

The FRAN hook

RSiena’s siena07() calls a simulation function named FRAN once per Robbins–Monro iteration. If alg$FRAN is a function, RSiena uses it directly. cusna_fran() builds such a function over the native simulator. Here it estimates the s50 friendship panel shipped with cusna:

library(RSiena)

net <- sienaDependent(array(unlist(s50$waves), dim = c(50, 50, 3)))
dat <- sienaDataCreate(net)
eff <- includeEffects(getEffects(dat), transTrip)

#>   effectNumber effectName          shortName include fix   test  initialValue
#> 1 21           transitive triplets transTrip TRUE    FALSE FALSE          0  
#>   parm
#> 1 0   

alg <- sienaAlgorithmCreate(projname = NULL, nsub = 2, n3 = 300, seed = 2026)

#> If you use this algorithm object, siena07 will create/use an output file Siena.txt .

alg$FRAN <- cusna_fran(
  waves        = s50$waves,                         # the same 0/1 wave matrices
  effect_names = c("density", "recip", "transTrip"),# in the RSiena effects order
  conditional  = TRUE)                              # must match `cond`

ans <- siena07(alg, data = dat, effects = eff, batch = TRUE,
               verbose = FALSE, silent = TRUE, useCluster = FALSE)
ans                              # a normal sienaFit

#> Estimates, standard errors and convergence t-ratios
#> 
#>                                    Estimate   Standard   Convergence 
#>                                                 Error      t-ratio   
#> 
#> Rate parameters: 
#>   0.1      Rate parameter period 1  6.3824  ( 1.0321   )             
#>   0.2      Rate parameter period 2  5.1734  ( 0.9202   )             
#> 
#> Other parameters: 
#>   1.  eval outdegree (density)     -2.6572  ( 0.1141   )   0.1342    
#>   2.  eval reciprocity              2.4041  ( 0.2037   )   0.0269    
#>   3.  eval transitive triplets      0.6305  ( 0.0692   )   0.1210    
#> 
#> Overall maximum convergence ratio:    0.3035 
#> 
#> 
#> Total of 521 iteration steps.
#> 

attr(alg$FRAN, "counter")$i      # simulation calls made by siena07()

#> [1] 521

(The short nsub/n3 keep this vignette quick; use RSiena’s defaults in practice.) The effect_names must list the included effects in the same order as the rows of the RSiena effects object, and conditional must match the algorithm’s cond setting (RSiena conditions on the observed distances by default for a single network); the closure stops with an explanatory error when the parameter vector does not match. Covariate effects (egoX/altX/simX/sameX) read the covariate argument. Simulation seeds come from R’s random number stream, which siena07() initialises from the algorithm’s seed, so the run above is reproducible.

Scope

• The closure simulates on the compiled native engine (its CPU backend in the R package) — no Python, no GPU required.
• One simulation is produced per call, matching RSiena’s simstats0c contract; batching happens across Robbins–Monro iterations, not within a call.
• The current bridge covers the structural and single-covariate effects named in effect_names; mapping an arbitrary siena07 effects object (multiple covariates, behavior co-evolution) onto the native descriptors is under development. For those models, use the standalone mom_estimate().
• Simulated networks are not handed back to RSiena (returnDeps); for goodness of fit on the native engine, use mom_estimate(..., return_nets = TRUE).

Which path to choose

Situation	Recommended path
Well-conditioned model, want maximum speed	standalone mom_estimate()
Nearly collinear / hard convergence	cusna_fran() + siena07()
Need RSiena’s exact estimator semantics	cusna_fran() + siena07()
Behavior co-evolution, multi-network	standalone mom_estimate() / mom_estimate_multinet()