R: Penalized Estimation of Multiple-Subject Vector Autoregressive Models

# Penalized Estimation of Multiple-Subject Vector Autoregressive Models

## Documentation for package ‘multivar’ version 1.4.0

- DESCRIPTION file
- Package README

## Help Pages

multivar-package | multivar: Penalized Estimation of Multiple-Subject Vector Autoregressive Models
build_matrix_spec | Build Design Matrix Specification
build_scenario_table | Reconstruct scenario-level multipliers used to build W
canonical.multivar | Canonical VAR Fitting Function for multivar
canonical.multivar-method | Canonical VAR Fitting Function for multivar
coef.multivar_fit | S3 Methods for multivar_fit Objects
compute_ebic | Compute EBIC for an array of fitted coefficient matrices
constructModel | Construct an object of class multivar
cv.multivar | Cross Validation for multivar
cv.multivar-method | Cross Validation for multivar
cv_blocked | Blocked Cross-Validation for multivar
cv_multivar | Cross-Validation Dispatcher for multivar
dat_multivar_sim | Simulated multi-VAR data.
estimate_initial_coefs | Estimate initial coefficients for adaptive weights
eval_multivar_performance | Summarize multivar performance against simulation truth (robust to missing parts)
expand_range | Expand Column Range to Indices
extract_multivar_hyperparams | Extract hyperparameter grid search results
get_common_cols | Get Common Effect Columns
get_common_effects | Helper Functions for Accessing multivar Results
get_common_tvp_cols | Get Common TVP Columns for a Period
get_dynamics | Helper Functions for Accessing multivar Results
get_dynamics_all_subjects | Helper Functions for Accessing multivar Results
get_dynamics_helpers | Helper Functions for Accessing multivar Results
get_n_periods | Get Number of Periods in TVP Model
get_period_rows | Get Row Range for a Subject's Period
get_subgrp_cols | Get Subgroup Effect Columns
get_subgrp_cols_for_subject | Get Subgroup Columns for a Subject
get_subject_rows | Get Row Range for a Subject
get_total_effects | Helper Functions for Accessing multivar Results
get_unique_cols | Get Unique Effect Columns for a Subject
get_unique_effects | Helper Functions for Accessing multivar Results
get_unique_tvp_cols | Get Unique TVP Columns for a Subject and Period
is_tvp | Check if Model is Time-Varying
lambda_grid | Build the lambda1 path (one column per penalty scenario)
maity_debiasing | Maity et al. (2022) MrLasso-style initial estimation
multivar-class | multivar object class
multivar_fit_methods | S3 Methods for multivar_fit Objects
multivar_sim | Simulate multivar data.
multivar_sim_breaks | Simulate multivar data with time-varying dynamics according to a latent growth curve.
multivar_sim_growth | Simulate multivar data with time-varying dynamics according to a latent growth curve.
multivar_sim_subgroups | Simulate multivar data with explicit subgroup structure
multivar_sim_tvp | Simulate multivar TVP data (piecewise or continuous)
plot.multivar_fit | S3 Methods for multivar_fit Objects
plot_cv_lambda_grid | Plot CV error over (lambda1, lambda2)
plot_results | Plot fitted multivar model results
plot_sim | Plot simulated multivar ground truth
plot_transition_mat | Plot arbitrary transition matrix
pretrained_var_stage1_glmnet | Stage 1 (glmnet): common block only, with optional per-equation intercepts
print.matrix_spec | Print method for matrix_spec
print.multivar_fit | S3 Methods for multivar_fit Objects
print_dynamics | Print dynamics matrix with clear formatting
print_dynamics_tvp | Print time-varying dynamics (TVP models)
print_edge_prevalence | Print edge prevalence across subjects
recover_intercepts | Recover Intercepts from Centered Data
select_by_ebic | Reselect best model using (E)BIC instead of MSFE
show-method | Default show method for an object of class multivar
show.multivar | Default show method for an object of class multivar
summary.multivar_fit | S3 Methods for multivar_fit Objects
summary_multivar | Summary for multivar fit objects
validate_matrix_spec | Validate Matrix Specification
var_sim_tvp | Simulate a time-varying VAR model (piecewise or continuous)