bayesRecon (version 1.0.2)

.core_reconc_BUIS: 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
)

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.

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.