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lame 1.3.4

• The handful of Gibbs samplers and helpers once adapted from amen have been rewritten from the underlying published methods, so the package carries no derived code and stays under the MIT license.
• Fixed a prior-scaling bug that could silently collapse the latent factors in asymmetric 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.
• A few documented options now do what they say: 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.
• Fitting no longer leaves a .Random.seed behind or touches options(warn).
• Fixed Rd markup flagged by CRAN.
• Time-varying coefficients (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.
• Set 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.

Known limitations

• Bipartite fit$U / fit$V are a single posterior draw (the rotation is unidentified); use fit$YPM or reconstruct_UVPM() for the stable multiplicative structure.
• In the bipartite dynamic_uv path, latent persistence rho_uv is only weakly identified – read it qualitatively. The unipartite path is unaffected.

lame 1.2.0

• Cleaned up ALS support and documentation.
• Raised the lame(method = "als") iteration cap and exposed dynamic ALS convergence component traces.
• Stabilized dynamic-UV MCMC by normalizing the raw 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().

lame 1.1.0

New features

• Fast, MCMC-free estimation via 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).

lame 1.0.0

Initial CRAN Submission

Features

• Cross-sectional network analysis via ame() with support for 6 distributional families: normal, binary, ordinal, Poisson, censored binary, and fixed rank nomination.
• Longitudinal network analysis via lame() with dynamic additive and multiplicative effects modeled as AR(1) processes.
• Support for both unipartite (square) and bipartite (rectangular) network structures.
• Covariate support: dyadic (Xdyad), row (Xrow), and column (Xcol) covariates with automatic design matrix construction.
• S3 methods: print(), summary(), coef(), vcov(), confint(), predict(), fitted(), residuals(), simulate(), plot().
• Visualization functions: trace_plot(), gof_plot(), ab_plot(), uv_plot() for MCMC diagnostics and model assessment.
• Goodness-of-fit via posterior predictive checks with gof().
• C++ acceleration via Rcpp and RcppArmadillo for core sampling routines.
• Handles changing actor compositions across time periods.

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.