# Longitudinal Additive and Multiplicative Effects Models for Networks

## Documentation for package ‘lame’ version 1.3.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G I L M N P R S T U V W X Y

## -- A --

absdiff | ERGM-style covariate helpers for ame() / lame()
ab_plot | Visualize sender and receiver random effects
ab_plot.ame_als | Additive-effects plot for an ame_als fit
addhealthc3 | AddHealth community 3 data
addhealthc9 | AddHealth community 9 data
als_dynamic_beta | Penalised ALS time-varying coefficient estimate
als_start_vals | Convert an ALS fit to MCMC starting values
ame | AME model fitting routine
ame_als | Fast (MCMC-free) AME estimation for a cross-sectional network
ame_als_bootstrap | Bootstrap uncertainty for the fast AME estimator
ame_als_refit | Refit a fast AME model with a warm start
ame_memory_settings | Display memory usage information for AME models
ame_memory_usage | Calculate memory usage of AME model components
ame_options | AME model fitting options
ame_parallel | Run AME model with multiple parallel chains
array_to_list | Convert array to list.
as_draws | Generic dispatcher for posterior::as_draws on lame fits
as_draws.ame | Convert an AME / LAME fit to a posterior draws object
as_draws.ame_als | Convert an AME / LAME fit to a posterior draws object
as_draws.lame | Convert an AME / LAME fit to a posterior draws object
as_lame_y | Convert a graph object to a lame-ready adjacency matrix
autoplot.ame | Ribbon plot of time-varying coefficients (or coefplot for static fits)
autoplot.ame_als | autoplot method for ALS fits
autoplot.lame | Ribbon plot of time-varying coefficients (or coefplot for static fits)
autoplot.lame_als | autoplot method for ALS fits

## -- B --

boot_ame | Bootstrap uncertainty for the fast AME estimator
boot_ame-no-fitted | fitted/residuals are not defined for a bootstrap object

## -- C --

check_format | Validate input data format for lame function
coef.als_dynamic_beta | Extract beta path from a penalised-ALS object
coef.ame | Extract model coefficients from AME model
coef.ame_als | Extract coefficients from a fast AME fit
coef.boot_ame | Point estimates from a fast AME bootstrap
coef.lame | Extract model coefficients from AME model
coldwar | Cold War data
combine_ame_chains | Combine multiple AME chains
compact_ame | Optimize AME model output for memory efficiency
compute_mcmc_diagnostics | Compute MCMC convergence diagnostics for multiple chains
compute_XtX_Xty_bip_cpp | Compute X'X and X'y for bipartite covariate regression
comtrade | Comtrade data
confint.ame | Bayesian credible intervals for AME model parameters
confint.ame_als | Confidence intervals for a fast AME fit
confint.boot_ame | Confidence intervals from a fast AME bootstrap
confint.lame | Bayesian credible intervals for AME model parameters

## -- D --

design_array_listwisedel | Computes the design socioarray of covariate values
detect_change_point | Detect potential change points in a dynamic_beta posterior path
dutchcollege | Dutch college data
dynamic_beta_prior_summary | Summarise the implied prior on a time-varying coefficient path

## -- E --

el2sm | Edgelist to sociomatrix
evaluate_heldout | Held-out predictive evaluation for an ame / lame fit

## -- F --

fitted.ame | Extract fitted values from AME model
fitted.ame_als | Extract fitted values from a fast AME fit
fitted.boot_ame | fitted/residuals are not defined for a bootstrap object
fitted.lame | Extract fitted values from LAME model
forecast_pit | Probability-integral-transform calibration check for h-step forecasts
formula.ame | formula() is not defined for an ame() / lame() fit
formula.lame | formula() is not defined for an ame() / lame() fit

## -- G --

get_design_rep | Create design array for replicate data
get_EZ_dynamic_beta_cpp | Compute EZ when beta is time-varying
get_fit_object | Get fitted object from MCMC results
get_start_vals | Get fitted object from MCMC results
glance | S3 generic for 'glance'
glance.ame | Glance method for fitted 'ame' / 'lame' objects
glance.ame_als | Glance method for fitted 'ame_als' / 'lame_als' objects
glance.lame | Glance method for fitted 'ame' / 'lame' objects
glance.lame_als | Glance method for fitted 'ame_als' / 'lame_als' objects
gof | Compute GOF statistics from saved posterior samples
gof_plot | Visualize goodness-of-fit statistics for AME and LAME models
gof_plot.ame_als | Goodness-of-fit check for an ame_als fit
gof_stats | Goodness of fit statistics
gof_stats_bipartite | Goodness of fit statistics for bipartite networks
gof_stats_unipartite | Goodness of fit statistics for unipartite networks
gof_temporal | Posterior-predictive temporal-trend test

## -- I --

init_dynamic_ab_cpp | Initialize dynamic additive effects with AR(1) structure
init_dynamic_positions | Initialize dynamic latent positions with AR(1) structure
IR90s | International relations in the 90s

## -- L --

lame | AME model fitting routine for longitudinal relational data
lame_als | Fast (MCMC-free) AME estimation for a longitudinal network
lame_multi | Multi-panel lame() with shared coefficients
lame_parallel | Run LAME (longitudinal AME) with multiple parallel chains
lame_resume | Resume a 'lame()' MCMC run from a checkpoint
lame_snap_als | Fast approximate dynamic snap-shift AME estimator
latent_positions | Extract latent positions as a tidy data frame
latent_positions.ame | Extract latent positions as a tidy data frame
latent_positions.ame_als | Extract latent positions as a tidy data frame
latent_positions.lame | Extract latent positions as a tidy data frame
lazegalaw | Lazega's law firm data
lfo | Exact rolling-origin leave-future-out cross-validation
list_to_array | Convert list to array
logLik.ame | Log-likelihood is not directly exposed for ame() / lame() fits
logLik.ame_als | Log-likelihood is not defined for a fast AME fit
logLik.lame | Log-likelihood is not directly exposed for ame() / lame() fits
loo | Generic dispatcher for loo / waic on ame / lame fits
loo.ame | Approximate leave-one-out cross-validation for AME / LAME fits
loo.ame_als | Approximate leave-one-out cross-validation for AME / LAME fits
loo.lame | Approximate leave-one-out cross-validation for AME / LAME fits

## -- M --

mhalf | Symmetric square root of a matrix

## -- N --

nobs.ame | Number of observed dyads in an AME / LAME fit
nobs.ame_als | Number of observed dyads in an ame_als fit
nobs.lame | Number of observed dyads in an AME / LAME fit
nodefactor | ERGM-style covariate helpers for ame() / lame()
nodematch | ERGM-style covariate helpers for ame() / lame()

## -- P --

per_actor_slopes | Post-MCMC per-actor time-varying slopes
plot.ame | Simple diagnostic plot for AME model fit
plot.ame_als | Plot the convergence of a fast AME fit
plot.lame | Plot diagnostics for a LAME model fit
posterior_options | Options for saving posterior samples during MCMC
posterior_quantiles | Extract posterior quantiles for model components
predict.ame | Predict method for AME models
predict.ame_als | Predictions from a fast AME fit
predict.lame | Predict method for LAME models
prediction_draws_long | Long-format draws of the linear predictor for marginaleffects-style use
print.als_dynamic_beta | Print method for penalised ALS time-varying beta
print.ame | Print method for AME model objects
print.ame.sim | Print methods for AME and LAME simulation objects
print.ame_als | Print an ame_als object
print.boot_ame | Print bootstrap results for a fast AME fit
print.gof_temporal | Print method for gof_temporal output
print.lame | Print method for LAME objects
print.lame.sim | Print methods for AME and LAME simulation objects
print.lame_multi | Print method for lame_multi
print.lfo_lame | Print method for lfo() results
print.per_actor_slopes | Print method for per_actor_slopes
print.summary.ame | Print method for summary.ame objects
print.summary.ame_als | Print a fast AME summary
print.summary.boot_ame | Summarize bootstrap results for a fast AME fit
print.summary.lame | Print method for summary.lame objects
prior_summary | Print the priors used by an AME / LAME / ame_als fit
prior_summary.ame | Print the priors used by an AME / LAME / ame_als fit
prior_summary.ame_als | Print the priors used by an AME / LAME / ame_als fit
prior_summary.default | Print the priors used by an AME / LAME / ame_als fit
prior_summary.lame | Print the priors used by an AME / LAME / ame_als fit
procrustes_align | Procrustes alignment of latent positions across time

## -- R --

rbeta_ab_bip_gibbs_cpp | Full bipartite Gibbs update for beta, a, b
read_log_lik | Read the per-iteration log-lik matrix back from on-disk chunks
reconstruct_EZ | Reconstruct EZ and UVPM matrices from AME model output
reconstruct_UVPM | Reconstruct EZ and UVPM matrices from AME model output
residuals.ame | Extract residuals from AME model
residuals.ame_als | Residuals from a fast AME fit
residuals.boot_ame | fitted/residuals are not defined for a bootstrap object
residuals.lame | Extract residuals from LAME model
rhat_dynamic_beta | Multivariate split-R-hat for dynamic_beta coefficient paths
rmvnorm | Simulation from a multivariate normal distribution
rSab_fc | Gibbs update for additive effects covariance
rUV_dynamic_bip_fc_cpp | Bipartite dynamic UV Gibbs update
rUV_dynamic_fc | Gibbs sampling of dynamic U and V with AR(1) evolution
rUV_dynamic_fc_cpp | Update dynamic latent positions using AR(1) process
rUV_dynamic_snap_fc | Gibbs sampling of dynamic U and V with snap-shift dynamics
rUV_dynamic_snap_fc_cpp | Update dynamic latent positions with snap-shift model selection
rUV_dynamic_t_fc | Gibbs sampling of dynamic U and V with heavy-tailed (Student-t) innovations
rUV_dynamic_t_fc_cpp | Update dynamic latent positions with heavy-tailed (Student-t) AR(1) innovations
rUV_sym_fc | Gibbs sampling of U and V
rZ_bin_bip_batch_cpp | Batch binary Z sampling across all time periods (bipartite, rho=0)
rZ_nrm_batch_cpp | Batch normal Z sampling across all time periods
rZ_nrm_fc | Simulate missing values in a normal AME model
rZ_pois_fc | Gibbs update for latent variable in a Poisson AME model

## -- S --

sampler_describe | Describe the estimator behind a fitted object
sampler_describe.ame | Describe the estimator behind a fitted object
sampler_describe.ame_als | Describe the estimator behind a fitted object
sampler_describe.boot_ame | Describe the estimator behind a fitted object
sampler_describe.lame | Describe the estimator behind a fitted object
sample_beta_dynamic_cpp | Sample the dynamic-block beta path via FFBS
sample_beta_static_cpp | Sample the static-block beta conditional on the dynamic path
sample_dynamic_ab_cpp | Sample dynamic additive effects with AR(1) evolution
sample_rho_ab_cpp | Sample AR(1) parameter for dynamic additive effects
sample_rho_beta_cpp | Sample the AR(1) rho for each dynamic block
sample_rho_uv | Sample AR(1) parameter for dynamic latent factors
sample_sigma_ab_cpp | Sample innovation variance for dynamic additive effects
sample_sigma_beta_cpp | Sample the AR(1) innovation sigma for each dynamic block
sample_sigma_uv | Sample innovation variance for dynamic latent factors
sampsonmonks | Sampson's monastery data
sheep | Sheep dominance data
simulate.ame | Simulate networks from a fitted AME model
simulate.ame_als | Simulate networks from a fitted ame_als model
simulate.lame | Simulate longitudinal networks from a fitted LAME model
simulate_posterior | Simulate posterior distributions from fitted AME model
simY_pois | Simulate a Poisson relational matrix
snap_category_summary | Summarize snap indices by actor category
snap_index_draws | Extract posterior draws of snap indices
snap_index_summary | Summarize posterior snap indices
snap_rank_summary | Summarize posterior rank uncertainty for snap years
summary.ame | Summary of an AME object
summary.ame.sim | Summary method for AME simulations
summary.ame_als | Summarize an ame_als object
summary.boot_ame | Summarize bootstrap results for a fast AME fit
summary.lame | Summary of a LAME object
summary.lame.sim | Summary method for LAME simulations

## -- T --

tidy | S3 generic for 'tidy'
tidy.ame | Tidy method for fitted 'ame' / 'lame' objects
tidy.ame_als | Tidy method for fitted 'ame_als' / 'lame_als' objects
tidy.boot_ame | Tidy method for a standalone bootstrap object ('boot_ame')
tidy.lame | Tidy method for fitted 'ame' / 'lame' objects
tidy.lame_als | Tidy method for fitted 'ame_als' / 'lame_als' objects
trace_plot | MCMC trace plots and density plots for AME/LAME model parameters

## -- U --

update.ame | Update an AME / LAME fit
update.ame_als | Update an 'ame_als' / 'lame_als' fit
update.lame | Update an AME / LAME fit
update.lame_als | Update an 'ame_als' / 'lame_als' fit
uv_plot | Visualize multiplicative effects (latent factors) from AME models

## -- V --

vcov.ame | Posterior covariance of AME model coefficients
vcov.ame_als | Sandwich covariance for the regression coefficients of a fast AME fit
vcov.boot_ame | Bootstrap covariance of the regression coefficients
vcov.lame | Posterior covariance of AME model coefficients
vignette_data | TIES sanctions data for vignettes

## -- W --

waic | Generic dispatcher for loo / waic on ame / lame fits
waic.ame | WAIC for AME / LAME fits
waic.ame_als | WAIC for AME / LAME fits
waic.lame | WAIC for AME / LAME fits

## -- X --

Xbeta | Linear combinations of submatrices of an array
Xbeta_bip_cpp | Compute Xbeta product for bipartite networks
Xcol | Column covariates
Xdyad | Dyadic covariates
Xrow | Row covariates

## -- Y --

Y | Relational matrix
YX_bin | binary relational data and covariates
YX_bin_list | Synthetic longitudinal binary relational data, list-form (latent-scale)
YX_bin_long | synthetic longitudinal binary relational data (latent-scale)
YX_cbin | Censored binary nomination data and covariates
YX_frn | Fixed rank nomination data and covariates
YX_nrm | normal relational data and covariates
YX_ord | ordinal relational data and covariates