Package index • lame

All functions

IR90s: International relations in the 90s

Xbeta(): Linear combinations of submatrices of an array

Xcol: Column covariates

Xdyad: Dyadic covariates

Xrow: Row covariates

Y: Relational matrix

YX_bin: binary relational data and covariates

YX_bin_list: Binary relational data list

YX_bin_long: binary relational data and covariates

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

YX_rrl: row-specific ordinal relational data and covariates

ab_plot(): Visualize sender and receiver random effects

addhealthc3: AddHealth community 3 data

addhealthc9: AddHealth community 9 data

ame(): AME model fitting routine

array_to_list(): Convert array to list.

check_format(): Check formatting of input objects into ame_repL function

coldwar: Cold War data

comtrade: Comtrade data

design_array(): Computes the design socioarray of covariate values

design_array_listwisedel(): Computes the design socioarray of covariate values

dutchcollege: Dutch college data

el2sm(): Edgelist to sociomatrix

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

gof_plot(): Visualize goodness-of-fit statistics for AME and LAME models

gof_stats(): Goodness of fit statistics

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

lazegalaw: Lazega's law firm data

ldZgbme(): log density for GBME models

list_to_array(): Convert list to array

llsrmRho(): SRM log likelihood evaluated on a grid of rho-values

mhalf(): Symmetric square root of a matrix

plot(<ame>): Plot comprehensive diagnostics for an AME model fit

plot(<lame>): Plot comprehensive diagnostics for a LAME model fit

precomputeX(): Precompute design matrix statistics

print(<ame>): Print method for AME objects

print(<ame.sim>): Print methods for AME and LAME simulation objects

print(<lame>): Print method for LAME objects

print(<summary.ame>): Print method for summary.ame objects

print(<summary.lame>): Print method for summary.lame objects

rSab_fc(): Gibbs update for additive effects covariance

rSuv_fc(): Gibbs update for multiplicative effects covariance

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_fc(): Gibbs sampling of U and V

rUV_rep_fc(): Gibbs sampling of U and V

rUV_sym_fc(): Gibbs sampling of U and V

rZ_bin_fc(): Simulate Z based on a probit model

rZ_cbin_fc(): Simulate Z given fixed rank nomination data

rZ_frn_fc(): Simulate Z given fixed rank nomination data

rZ_nrm_fc(): Simulate missing values in a normal AME model

rZ_ord_fc(): Simulate Z given the partial ranks

rZ_pois_fc(): Gibbs update for latent variable in a Poisson AME model

rZ_rrl_fc(): Simulate Z given relative rank nomination data

rZ_tob_fc(): Simulate Z based on a tobit model

raSab_bin_fc(): Simulate a and Sab from full conditional distributions under bin likelihood

raSab_cbin_fc(): Simulate a and Sab from full conditional distributions under the cbin likelihood

raSab_frn_fc(): Simulate a and Sab from full conditional distributions under frn likelihood

rbeta_ab_fc(): Conditional simulation of additive effects and regression coefficients

rbeta_ab_rep_fc(): Gibbs sampling of additive row and column effects and regression coefficient with independent replicate relational data

rmvnorm(): Simulation from a multivariate normal distribution

rrho_fc(): Griddy Gibbs update for dyadic correlation

rrho_mh(): Metropolis update for dyadic correlation

rrho_mh_rep(): Metropolis update for dyadic correlation with independent replicate data

rs2_fc(): Gibbs update for dyadic variance

rs2_rep_fc(): Gibbs update for dyadic variance with independent replicate relational data

rwish(): Simulation from a Wishart distribution

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_uv(): Sample AR(1) parameter for dynamic latent factors

sample_sigma_ab_cpp(): Sample innovation variance for dynamic additive effects

sample_sigma_uv(): Sample innovation variance for dynamic latent factors

sampsonmonks: Sampson's monastery data

sheep: Sheep dominance data

simY_bin(): Simulate a network, i.e. a binary relational matrix

simY_frn(): Simulate an relational matrix based on a fixed rank nomination scheme

simY_nrm(): Simulate a normal relational matrix

simY_ord(): Simulate an ordinal relational matrix

simY_pois(): Simulate a Poisson relational matrix

simY_rrl(): Simulate an relational matrix based on a relative rank nomination scheme

simY_tob(): Simulate a tobit relational matrix

simZ(): Simulate Z given its expectation and covariance

simulate(<ame>): Simulate networks from a fitted AME model

simulate(<lame>): Simulate longitudinal networks from a fitted LAME model

sm2el(): Sociomatrix to edgelist

summary(<ame>): Summary of an AME object

summary(<ame.sim>): Summary method for AME simulations

summary(<lame>): Summary of a LAME object

summary(<lame.sim>): Summary method for LAME simulations

trace_plot(): MCMC trace plots and density plots for AME/LAME model parameters

uv_plot(): Visualize multiplicative effects (latent factors) from AME models

zscores(): rank-based z-scores