NEWS

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lame 1.3.5

• lfo() refits no longer carry two intercepts. The stored design (fit$Xlist) already includes the intercept slice, and lfo() passed it back through Xdyad, so each refit added a second, exactly collinear intercept; the stored slice is now stripped before refitting.
• lame() no longer errors when called through a wrapper that forwards ... (or with only positional arguments); the model.name partial-match guard assumed the call had names.
• uv_plot() now honors layout = "circle" for dynamic snapshot plots; it was silently drawing a biplot. Also corrected the latent-space description in the help to the inner-product (eigenmodel) interpretation.
• simulate_posterior() now returns latent-factor and coefficient draws for dynamic fits (dynamic_uv, dynamic_beta) instead of erroring, and its bipartite draws include the interaction matrix G (they previously reconstructed U V' rather than U G V'). Credible-interval forecasts keep their actor names.
• Fixed the bundled sampsonmonks data: the three liking layers (like_m2, like_m1, like) had their actors in a different order from the array's row/column names (a problem inherited from amen), so those layers were mislabeled. All ten layers now share the same actor order.
• ab_plot(effect = "both") now works for ame and lame fits, drawing the sender and receiver effects as two panels (for dynamic fits, in the snapshot views); it previously only worked for ame_als fits.
• ame() and lame() gained a multistart argument that is forwarded to the static ALS estimators when method = "als"; it used to be dropped with an unused-argument error (lame()) or warning (ame()).
• symmetric = TRUE with the rank-nomination families (cbin, frn) is now rejected with an explanation; it used to route silently through the directed samplers.
• latent_positions() and procrustes_align() now say plainly that alignment is across the periods of one fit, and how to align two fits by hand.
• Poisson fits with many zero cells no longer pin the actor effects and latent factors at zero and inflate the fitted level (about 3x at 5% density, 1.2-1.4x at 20%): their prior scale vanished as zeros dominated. ame() also gained a level move so the predicted total settles quickly; see the notes in ?ame.
• Fixed a scale drift in bipartite dynamic_uv fits. Only the product U G V' is identified, and the leftover scale used to wander: G shrank toward zero while the latent coordinates grew, until they hit prior$uv_max_abs and the sampler began rejecting 20-40% of its own U/V proposals on realistic panel sizes. The latent coordinates are now kept at or below unit scale, with G absorbing the scale, which leaves U G V' untouched. For normal outcomes each margin is held at unit scale, which also stops the slow drift of scale into the interaction matrix on long dynamic_G chains, so the raw coordinates and the persistence parameters stay readable. For binary and Poisson outcomes the coordinates are only ever shrunk: a saturated likelihood wants the product to grow without bound, and pulling the coordinates back up would push all of that growth into G. Estimates of the identified quantities were not affected, but the raw coordinate scale was arbitrary and sampling efficiency suffered. The warning for this case now says what to do instead of suggesting a longer chain, which made it worse.
• Longitudinal fits estimate the latent-persistence parameters (rho_uv, sigma_uv) more reliably: the unipartite sampler now updates them every sweep instead of every tenth, matching the earlier bipartite fix, so they no longer sit near their starting value on default-length chains.
• Warm-starting a dynamic MCMC run from an ALS fit now carries the dynamic hyperparameters along: get_start_vals() (and the bipartite path) pass rho_uv, sigma_uv, rho_ab, and sigma_ab through instead of dropping them.
• Dynamic ALS fits (lame(method = "als") with dynamic_uv, dynamic_ab, or dynamic_beta) now report the innovation scales sigma_uv, sigma_ab, and sigma_beta implied by their fitted paths, so als_start_vals() can hand them to a warm-started MCMC run; they used to be NULL, which left the slowest-mixing parameters of a sparse dynamic panel at their cold defaults. Read them as starting values, not estimates: the smoothness penalty shrinks the fitted paths, so the implied scales run at roughly half the posterior scale. The persistences rho_uv, rho_ab, and rho_beta on a dynamic ALS fit are the values its penalty used, not estimates; absent a prior they now sit at the same prior means the MCMC uses (0.9, 0.8, and 0.8) rather than at 0.95.
• ALS fits with R > 0 expose the fitted multiplicative surface as UVPM (and ULUPM for symmetric fits), matching the MCMC fit objects.
• Dynamic ALS (lame(method = "als") with dynamic effects) no longer runs away on sparse binary or Poisson panels. It now reweights only once the block updates have settled or spent their step budget, keeps a reweighted step only when the true penalized deviance falls, and falls back to the best state on an overshoot, the same scheme the static route uses; the non-normal families also carry a level prior on the dynamic sender and receiver paths. A warning still fires if a block update fails to descend, and now says so plainly.
• dynamic_beta combined with dynamic_ab now gives the additive effects a real AR(1): the per-period draws pool across neighbouring years and rho_ab / sigma_ab are sampled rather than sitting at their starting values.
• Bipartite dynamic_G = TRUE fits can no longer be poisoned by a single non-finite state draw. The time-varying interaction matrix is accepted only when every element is finite, its variance draw keeps the current value when the state path is not finite, and the chain falls back to the last finite draw otherwise; before, one bad draw made every later update fail and the fit returned non-finite values for the rest of the run.
• Tidied up the documentation and test suite.

Known limitations

• In bipartite dynamic_G = TRUE fits with a binary or Poisson outcome, the raw latent coordinates can drift toward zero over long chains while G grows to compensate, and rho_G may then sit near its upper bound. The fitted product U G V' (and so EZ, YPM, and the forecasts) is unaffected; read the raw coordinates and rho_G qualitatively in that case.

lame 1.3.4 (2026-08-04)

• 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.