IR90s                   International relations in the 90s
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                       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
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_lame_y               Convert a graph object to a lame-ready
                        adjacency matrix
autoplot.ame_als        autoplot method for ALS fits
autoplot.lame           Ribbon plot of time-varying coefficients (or
                        coefplot for static fits)
boot_ame-no-fitted      fitted/residuals are not defined for a
                        bootstrap object
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
coldwar                 Cold War data
combine_ame_chains      Combine multiple AME chains
compact_ame             Optimize AME model output for memory efficiency
compute_XtX_Xty_bip_cpp
                        Compute X'X and X'y for bipartite covariate
                        regression
compute_mcmc_diagnostics
                        Compute MCMC convergence diagnostics for
                        multiple chains
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
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
el2sm                   Edgelist to sociomatrix
evaluate_heldout        Held-out predictive evaluation for an ame /
                        lame fit
fitted.ame              Extract fitted values from AME model
fitted.ame_als          Extract fitted values from a fast AME fit
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
get_EZ_dynamic_beta_cpp
                        Compute EZ when beta is time-varying
get_design_rep          Create design array for replicate data
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
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
init_dynamic_ab_cpp     Initialize dynamic additive effects with AR(1)
                        structure
init_dynamic_positions
                        Initialize dynamic latent positions with AR(1)
                        structure
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
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
loo                     Generic dispatcher for loo / waic on ame / lame
                        fits
loo.ame                 Approximate leave-one-out cross-validation for
                        AME / LAME fits
mhalf                   Symmetric square root of a matrix
nobs.ame                Number of observed dyads in an AME / LAME fit
nobs.ame_als            Number of observed dyads in an ame_als fit
nodematch               ERGM-style covariate helpers for ame() / lame()
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_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.lame      Print method for summary.lame objects
prior_summary           Print the priors used by an AME / LAME /
                        ame_als fit
procrustes_align        Procrustes alignment of latent positions across
                        time
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
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
residuals.ame           Extract residuals from AME model
residuals.ame_als       Residuals from a fast AME fit
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
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
sampler_describe        Describe the estimator behind a fitted object
sampsonmonks            Sampson's monastery data
sheep                   Sheep dominance data
simY_pois               Simulate a Poisson relational matrix
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
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
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')
trace_plot              MCMC trace plots and density plots for AME/LAME
                        model parameters
update.ame              Update an AME / LAME fit
update.ame_als          Update an 'ame_als' / 'lame_als' fit
uv_plot                 Visualize multiplicative effects (latent
                        factors) from AME models
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
vignette_data           TIES sanctions data for vignettes
waic.ame                WAIC for AME / LAME fits
