dot-core_reconc_BUIS: Core Reconciliation via Bayesian Universality Information...

In bayesRecon: Probabilistic Reconciliation via Conditioning

Core Reconciliation via Bayesian Universality Information Sharing

Description

Internal function that performs the core reconciliation logic for the BUIS method, which reconciles forecasts using importance sampling based on both hierarchical and equality constraints through a Bayesian framework.

Usage

.core_reconc_BUIS(
  A,
  H,
  G,
  B,
  upper_base_fc_H,
  in_typeH,
  distr_H,
  upper_base_fc_G,
  in_typeG,
  distr_G,
  .comp_w = .compute_weights,
  suppress_warnings = FALSE,
  return_upper = TRUE
)

Arguments

A	Matrix defining the overall hierarchy.
H	Matrix defining hierarchical constraints.
G	Matrix defining general linear constraints.
B	Matrix of bottom level base forecast samples.
upper_base_fc_H	List of upper base forecasts for hierarchical constraints.
in_typeH	Character string specifying input type for H forecasts ('pmf', 'samples', or 'params').
distr_H	Character string specifying distribution type for H forecasts ('poisson' or 'nbinom').
upper_base_fc_G	List of upper base forecasts for general constraints.
in_typeG	Character string specifying input type for G forecasts ('pmf', 'samples', or 'params').
distr_G	Character string specifying distribution type for G forecasts ('poisson' or 'nbinom').
.comp_w	Function to compute weights for importance sampling. Default is .compute_weights.
suppress_warnings	Logical. If TRUE, suppresses warnings about sample quality. Default is FALSE.

Value

A list containing:

• bottom_rec: List with reconciled bottom forecasts (pmf and/or samples).

• upper_rec_H: List with reconciled upper forecasts for H constraints.

• upper_rec_G: List with reconciled upper forecasts for G constraints.

bayesRecon documentation built on March 8, 2026, 9:08 a.m.