Overview | Mathematical Background | VAR(1) Marginals | Gaussian Copula | How the Models Differ | Per-variable (mixed) margins | Time-varying coefficients and scales | Fitting All Three Models | LOO-CV Comparison | Extracting Tidy Summaries | HMM-Specific Outputs | Clinical Interpretation | When to Use Which Model
Package: dcvar 0.9.2
Benedikt Lugauer
dcvar: Dynamic Copula VAR Models for Time-Varying Dependence
Fits Bayesian copula vector autoregressive models for bivariate time series with dynamic, regime-switching, and constant dependence structures. The package includes simulation, data preparation, estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior summaries, diagnostics, trajectory extraction, fitted and predictive summaries, and approximate leave-one-out cross-validation model comparison for supported fits. For Bayesian computation and model comparison, see Carpenter et al. (2017) <doi:10.18637/jss.v076.i01> and Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.
Authors:
dcvar_0.9.2.tar.gz
dcvar_0.9.2.zip(r-4.7)dcvar_0.9.2.zip(r-4.6)dcvar_0.9.2.zip(r-4.5)
dcvar_0.9.2.tgz(r-4.6-any)dcvar_0.9.2.tgz(r-4.5-any)
dcvar_0.9.2.tar.gz(r-4.7-any)dcvar_0.9.2.tar.gz(r-4.6-any)
dcvar_0.9.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dcvar/json (API)
| # Install 'dcvar' in R: |
| install.packages('dcvar', repos = c('https://benlug.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/benlug/dcvar/issues
Last updated from:05574041a0. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 295 | ||
| source / vignettes | OK | 213 | ||
| linux-release-x86_64 | OK | 280 | ||
| macos-release-arm64 | OK | 188 | ||
| macos-oldrel-arm64 | OK | 153 | ||
| windows-devel | OK | 244 | ||
| windows-release | OK | 181 | ||
| windows-oldrel | OK | 233 | ||
| wasm-release | OK | 205 |
Exports:aggregate_metricsapplicability_checkcompute_param_metricscompute_rho_metricscovariate_effectsdcvardcvar_comparedcvar_constantdcvar_covariatedcvar_diagnosticsdcvar_hmmdcvar_multileveldcvar_semdcvar_stan_pathdependence_summarydrawshmm_state_paramshmm_statesinterpret_rho_trajectorylatent_statesphi_trajectorypit_testpit_valuesplot_diagnosticsplot_hmm_statesplot_latent_statesplot_phiplot_phi_trajectoryplot_pitplot_ppcplot_random_effectsplot_rhoplot_sigma_trajectoryplot_trajectoriesprepare_constant_dataprepare_dcvar_covariate_dataprepare_dcvar_dataprepare_hmm_dataprepare_multilevel_dataprepare_sem_datarandom_effectsrho_constantrho_decreasingrho_double_steprho_increasingrho_random_walkrho_scenariorho_steprho_trajectorysigma_trajectorysimulate_breakpoint_datasimulate_dcvarsimulate_dcvar_multilevelsimulate_dcvar_semvar_params
Dependencies:abindbackportsbayesplotBHcallrcheckmateclicpp11descdistributionaldplyrfarvergenericsggplot2ggridgesgluegridExtragtableinlineisobandlabelinglifecycleloomagrittrmatrixStatsnumDerivotelpatchworkpillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanS7scalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr
Comparing Copula VAR Models
Last update: 2026-06-13
Started: 2026-03-15
Getting Started with dcvar
Installation | Simulating Data | Fitting the DC-VAR Model | Inspecting Results | Fitted Values and Predictions | Tidy Parameter Summary | Clinical Interpretation | Visualisation | LOO-CV Model Comparison
Last update: 2026-04-26
Started: 2026-03-15
Simulation Study Tools
Trajectory Generators | Visualising Trajectories | Simulating Data | Breakpoint Data Simulator | Data Preparation | Parameter Recovery Metrics | Running a Mini Simulation Study
Last update: 2026-04-22
Started: 2026-03-15
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Aggregate metrics across simulation replications | aggregate_metrics |
| Check whether the flexible-margin copula-VAR is appropriate for the data | applicability_check |
| Convert a dcvar fit summary to a data frame | as.data.frame.dcvar_model_fit |
| Compute scalar parameter recovery metrics | compute_param_metrics |
| Compute rho trajectory recovery metrics | compute_rho_metrics |
| Extract covariate effect summaries | covariate_effects covariate_effects.dcvar_covariate_fit covariate_effects.default |
| Fit the DC-VAR model | dcvar |
| Compare multiple fitted models using LOO-CV | dcvar_compare |
| Fit the constant copula model | dcvar_constant |
| S3 methods for dcvar_constant_fit objects | coef.dcvar_constant_fit dcvar_constant_fit-methods plot.dcvar_constant_fit print.dcvar_constant_fit summary.dcvar_constant_fit |
| Fit the covariate DC-VAR model | dcvar_covariate |
| S3 methods for covariate DC-VAR fits | coef.dcvar_covariate_fit dcvar_covariate_fit-methods plot.dcvar_covariate_fit print.dcvar_covariate_fit summary.dcvar_covariate_fit |
| Extract MCMC diagnostics | dcvar_diagnostics dcvar_diagnostics.dcvar_model_fit dcvar_diagnostics.default |
| S3 methods for dcvar_fit objects | coef.dcvar_fit dcvar_fit-methods plot.dcvar_fit print.dcvar_fit summary.dcvar_fit |
| Fit the HMM copula model | dcvar_hmm |
| S3 methods for dcvar_hmm_fit objects | coef.dcvar_hmm_fit dcvar_hmm_fit-methods plot.dcvar_hmm_fit print.dcvar_hmm_fit summary.dcvar_hmm_fit |
| Fit an experimental multilevel copula VAR(1) model | dcvar_multilevel |
| S3 methods for dcvar_multilevel_fit objects | coef.dcvar_multilevel_fit dcvar_multilevel_fit-methods plot.dcvar_multilevel_fit print.dcvar_multilevel_fit print.dcvar_multilevel_tv_fit summary.dcvar_multilevel_fit summary.dcvar_multilevel_tv_fit |
| Fit an experimental SEM copula VAR(1) model | dcvar_sem |
| S3 methods for dcvar_sem_fit objects | coef.dcvar_sem_fit dcvar_sem_fit-methods plot.dcvar_sem_fit print.dcvar_sem_fit print.dcvar_sem_tv_fit summary.dcvar_sem_fit summary.dcvar_sem_tv_fit |
| Get path to bundled Stan model file | dcvar_stan_path |
| S3 methods for dcvar_tv_fit objects | coef.dcvar_tv_fit dcvar_tv_fit-methods plot.dcvar_tv_fit print.dcvar_tv_fit summary.dcvar_tv_fit |
| Extract a unified dependence summary | dependence_summary dependence_summary.dcvar_constant_fit dependence_summary.dcvar_covariate_fit dependence_summary.dcvar_fit dependence_summary.dcvar_hmm_fit dependence_summary.dcvar_multilevel_fit dependence_summary.dcvar_multilevel_tv_fit dependence_summary.dcvar_sem_fit dependence_summary.dcvar_sem_tv_fit dependence_summary.default |
| Extract posterior draws | draws draws.dcvar_model_fit draws.default |
| One-step-ahead fitted values for a Markov-switching HMM fit | fitted.dcvar_hmm_fit |
| Fitted values from a copula VAR model | fitted.dcvar_model_fit fitted.dcvar_multilevel_fit fitted.dcvar_multilevel_tv_fit fitted.dcvar_sem_fit fitted.dcvar_tv_fit |
| Extract per-state parameters from a Markov-switching HMM fit | hmm_state_params hmm_state_params.dcvar_hmm_fit hmm_state_params.default |
| Extract HMM state information | hmm_states hmm_states.dcvar_hmm_fit hmm_states.default |
| Interpret a rho trajectory in clinical terms | interpret_rho_trajectory |
| Extract latent states from a SEM fit | latent_states latent_states.dcvar_sem_fit latent_states.default |
| Compute LOO-CV for a fitted model | loo.dcvar loo.dcvar_constant_fit loo.dcvar_covariate_fit loo.dcvar_fit loo.dcvar_hmm_fit loo.dcvar_multilevel_fit loo.dcvar_sem_fit |
| Extract the time-varying VAR coefficient trajectories | phi_trajectory phi_trajectory.dcvar_multilevel_tv_fit phi_trajectory.dcvar_sem_tv_fit phi_trajectory.dcvar_tv_fit phi_trajectory.default |
| KS test for PIT uniformity | pit_test pit_test.dcvar_model_fit pit_test.default |
| Extract PIT values from a fitted model | pit_values pit_values.dcvar_hmm_fit pit_values.dcvar_model_fit pit_values.dcvar_tv_fit pit_values.default |
| Plot MCMC diagnostics | plot_diagnostics |
| Plot HMM state posteriors | plot_hmm_states |
| Plot estimated latent states with credible intervals | plot_latent_states |
| Plot VAR(1) coefficient matrix as a heatmap | plot_phi |
| Plot the time-varying VAR coefficient trajectories | plot_phi_trajectory |
| Plot PIT histograms | plot_pit |
| Posterior predictive check for residual correlations | plot_ppc |
| Plot random effects (caterpillar plot) | plot_random_effects |
| Plot the rho trajectory with credible intervals | plot_rho |
| Plot the time-varying residual scale trajectories | plot_sigma_trajectory |
| Plot and compare multiple rho trajectory shapes | plot_trajectories |
| Prediction intervals for a Markov-switching HMM fit | predict.dcvar_hmm_fit |
| One-step-ahead predictions from a copula VAR model | predict.dcvar_model_fit predict.dcvar_multilevel_fit predict.dcvar_multilevel_tv_fit predict.dcvar_sem_fit predict.dcvar_tv_fit |
| Prepare data for the constant copula model | prepare_constant_data |
| Prepare data for the covariate DC-VAR model | prepare_dcvar_covariate_data |
| Prepare data for the DC-VAR model | prepare_dcvar_data |
| Prepare data for the HMM copula model | prepare_hmm_data |
| Prepare data for the multilevel copula VAR model | prepare_multilevel_data |
| Prepare data for the SEM copula VAR model | prepare_sem_data |
| Print a flexible-margin applicability check | print.dcvar_applicability |
| Print a dcvar_constant_summary object | print.dcvar_constant_summary |
| Print a dcvar_covariate_summary object | print.dcvar_covariate_summary |
| Print a dcvar_hmm_summary object | print.dcvar_hmm_summary |
| Print a dcvar_multilevel_summary object | print.dcvar_multilevel_summary |
| Print a dcvar_multilevel_tv_summary object | print.dcvar_multilevel_tv_summary |
| Print a dcvar_sem_summary object | print.dcvar_sem_summary |
| Print a dcvar_sem_tv_summary object | print.dcvar_sem_tv_summary |
| Print a dcvar_summary object | print.dcvar_summary |
| Print a dcvar_tv_summary object | print.dcvar_tv_summary |
| Extract random effects from a multilevel fit | random_effects random_effects.dcvar_multilevel_fit random_effects.default |
| Generate a constant rho trajectory | rho_constant |
| Generate a logistically decreasing rho trajectory | rho_decreasing |
| Generate a double-breakpoint (relapse pattern) rho trajectory | rho_double_step |
| Generate a logistically increasing rho trajectory | rho_increasing |
| Generate a random walk rho trajectory on the Fisher-z scale | rho_random_walk |
| Get a named trajectory scenario | rho_scenario |
| Generate a single-breakpoint (step function) rho trajectory | rho_step |
| Extract the rho trajectory with credible intervals | rho_trajectory rho_trajectory.dcvar_constant_fit rho_trajectory.dcvar_covariate_fit rho_trajectory.dcvar_fit rho_trajectory.dcvar_hmm_fit rho_trajectory.dcvar_multilevel_fit rho_trajectory.dcvar_multilevel_tv_fit rho_trajectory.dcvar_sem_fit rho_trajectory.dcvar_sem_tv_fit rho_trajectory.default |
| Extract the time-varying residual scale trajectories | sigma_trajectory sigma_trajectory.dcvar_multilevel_tv_fit sigma_trajectory.dcvar_sem_tv_fit sigma_trajectory.dcvar_tv_fit sigma_trajectory.default |
| Simulate data with a breakpoint rho trajectory | simulate_breakpoint_data |
| Simulate data from a copula VAR(1) model | simulate_dcvar |
| Simulate data from a multilevel copula VAR(1) model | simulate_dcvar_multilevel |
| Simulate data from a SEM copula VAR(1) model | simulate_dcvar_sem |
| Extract VAR(1) parameter summaries | var_params var_params.dcvar_hmm_fit var_params.dcvar_model_fit var_params.dcvar_multilevel_fit var_params.dcvar_multilevel_tv_fit var_params.dcvar_sem_fit var_params.dcvar_sem_tv_fit var_params.dcvar_tv_fit var_params.default |