| exdqlm-package | R Documentation |
Bayesian quantile-regression tools for dynamic state-space models and static regression under the extended asymmetric Laplace error distribution (exAL).
The package centers on native dynamic quantile state-space modeling for
univariate time series and also provides a static exAL regression workflow.
Across these settings, exdqlm combines model
construction helpers, multiple Bayesian inference engines, shrinkage priors
for static coefficients, and post hoc synthesis of several fitted quantiles.
Dynamic/state-space quantile modeling via
exdqlmLDVB() and exdqlmMCMC(), with legacy exdqlmISVB()
retained for backward compatibility and transfer-function extensions
through exdqlmTransferLDVB(), exdqlmTransferMCMC(), and legacy
exdqlmTransferISVB().
Static Bayesian exAL regression via exalStaticLDVB() and
exalStaticMCMC(), with static fitted-quantile and coefficient
summaries through exalStaticDiagnostics().
Modular state-space construction via polytrendMod(), seasMod(),
and regMod().
Multi-quantile post-processing via
quantileSynthesis() for post hoc posterior-predictive
synthesis from separately fitted quantiles into a unified
predictive distribution.
Dynamic Bayesian quantile state-space inference with LDVB as the main VB engine, MCMC for posterior simulation, and legacy ISVB retained for compatibility and historical comparisons.
A unified package covering both dynamic exDQLM models and static exAL regression.
Static shrinkage priors including ridge, regularized horseshoe
("rhs"), and rhs_ns.
Reduced AL/DQLM paths through dqlm.ind = TRUE in both dynamic and
static APIs.
Standardized VB diagnostics traces via
fit$diagnostics$vb_trace for ELBO, sigma, gamma, and
convergence deltas across VB engines.
Conservative automatic warmup defaults for the most failure-prone
shared blocks: RHS-family tau scheduling plus exAL
(sigma, gamma) warmup in VB and MCMC entry points, with explicit
controls available only when users need to override the defaults.
Optional C++ acceleration for selected state-space computations.
Dynamic diagnostics report CRPS through a finite integrated
quantile-score approximation over posterior predictive empirical
quantiles, with user-configurable quantile levels and weights in
exdqlmDiagnostics().
Held-out forecast diagnostics are available for forecast objects
through exdqlmForecastDiagnostics().
Static diagnostics store fitted-quantile summaries and coefficient
intervals, with plot(..., type = "coefficients") available for
comparing static LDVB/MCMC coefficient summaries.
Dynamic KL normality diagnostics are deterministic for fixed fitted
objects and no longer depend on stochastic reference samples. The
top-level diagnostic object exposes KL as the primary calibration
diagnostic and keeps advanced KL sensitivity details under
kl.details.
options(exdqlm.use_cpp_kf = TRUE|FALSE) – C++ Kalman bridge (optional; default TRUE).
options(exdqlm.compute_elbo = TRUE|FALSE) – Compute ELBO (optional; default TRUE).
options(exdqlm.tol_elbo = numeric) – Positive ELBO convergence tolerance used when
exdqlm.compute_elbo = TRUE; smaller values enforce stricter ELBO stabilization checks
(default 1e-6).
options(exdqlm.use_cpp_builders = TRUE|FALSE) – C++ model builders (optional; default FALSE).
options(exdqlm.use_cpp_samplers = TRUE|FALSE) – C++ samplers (optional; default FALSE).
options(exdqlm.use_cpp_postpred = TRUE|FALSE) – C++ posterior predictive sampler (optional; default FALSE).
options(exdqlm.use_cpp_mcmc = TRUE|FALSE) – MCMC backend routing (optional; default TRUE).
options(exdqlm.cpp_mcmc_mode = "strict"|"fast") – strict keeps legacy R-kernel parity; fast enables C++ FFBS in MCMC (default "fast").
options(exdqlm.cpp_threads = numeric) – Positive integer thread cap for eligible
OpenMP-enabled C++ paths (1L forces single-thread; default 1L).
Maintainer: Raquel Barata raquel.a.barata@gmail.com
Authors:
Raquel Barata raquel.a.barata@gmail.com
Antonio Aguirre
Other contributors:
Raquel Prado [thesis advisor]
Bruno Sanso [thesis advisor]
Useful links:
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