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amen have been rewritten from the underlying published
methods, so the package carries no derived code and stays under the MIT
license.ame() fits with
R > 0; fits now match amen.dynamic_ab fits carry per-period posterior SDs, so
ab_plot(fit, plot_type = "ribbon") draws a real credible
band.log_lik_method = "augmented", gof(nsim = NULL)
(uses all draws), and bootstrap error bars in ab_plot() for
ame_als fits. Dropped an unused argument and fixed some
stale help text..Random.seed behind or
touches options(warn).dynamic_beta) compose freely
with the multiplicative latent factors (R > 0) and
additive sender/receiver effects, for every family and network type.
Coefficients can follow AR(1), random-walk, or Matern dynamics, and
predict() carries that drift into its forecasts.netify objects can be passed straight to
ame(), lame(), and the ALS fitters; a
network’s symmetric attribute is honored automatically, and
named covariates are aligned to Y by actor.posterior_opts = list(save_UV = TRUE) to keep the
per-draw latent factors (and the bipartite mixing matrix), so
latent_positions(), uv_plot(), and the
goodness-of-fit tools report posterior uncertainty directly. Symmetric
fits now store V_samples (U L per draw)
alongside U_samples, so the per-draw latent similarity
U L U' is reconstructable via
simulate_posterior(fit, "UV").summary() on a lame fit exposes the
coefficient table under $coefficients, matching
summary.ame and the broom/lm
idiom.fit$U / fit$V are a single
posterior draw (the rotation is unidentified); use fit$YPM
or reconstruct_UVPM() for the stable multiplicative
structure.dynamic_uv path, latent persistence
rho_uv is only weakly identified – read it qualitatively.
The unipartite path is unaffected.lame(method = "als") iteration cap and
exposed dynamic ALS convergence component traces.U/V coordinate scale, centering additive
row/column effects, pooling very sparse additive effects toward the
prior mean, and carrying ALS fits into MCMC with
als_start_vals().ame_als() and
lame_als(): an alternating least squares / IRLS point
estimator for the normal, binary, and Poisson families. Supports
parametric-bootstrap and sandwich standard errors and the full S3 method
set (coef(), vcov(), confint(),
predict(), tidy(), glance(), and
the diagnostic plots).ame() with support
for 6 distributional families: normal, binary, ordinal, Poisson,
censored binary, and fixed rank nomination.lame() with dynamic
additive and multiplicative effects modeled as AR(1) processes.Xdyad), row
(Xrow), and column (Xcol) covariates with
automatic design matrix construction.print(), summary(),
coef(), vcov(), confint(),
predict(), fitted(), residuals(),
simulate(), plot().trace_plot(),
gof_plot(), ab_plot(), uv_plot()
for MCMC diagnostics and model assessment.gof().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.