Build the workflow bundle used by the package fitters

bundle() is the main workflow constructor. It converts raw inputs, a formula/data pair, or an already prepared bundle into the canonical object consumed by mcmc, dpmix, dpmgpd, dpmix.causal, and dpmgpd.causal.

Usage

bundle(
  y = NULL,
  X = NULL,
  treat = NULL,
  data = NULL,
  formula = NULL,
  GPD = FALSE,
  ...
)

Arguments

y

Either a response vector or an existing bundle.

X

Optional design matrix/data.frame.

treat

Optional binary treatment indicator.

data

Optional data.frame used with formula.

formula

Optional formula.

GPD

Logical; include GPD tail in build mode.

...

Additional arguments passed to build_nimble_bundle() or build_causal_bundle().

Value

A "causalmixgpd_bundle" for one-arm models or a "causalmixgpd_causal_bundle" for causal models. The bundle stores code-generation inputs, monitor policy, and default MCMC settings, but it does not run MCMC.

Details

For one-arm models the returned object represents a bulk Dirichlet process mixture, optionally augmented with a spliced generalized Pareto tail. For causal models the returned object contains two arm-specific outcome bundles plus an optional propensity score block.

The workflow is:

1. prepare a bundle with bundle(),

2. run posterior sampling with mcmc or one of the dpmix*/dpmgpd* wrappers,

3. inspect the fitted object with summary.mixgpd_fit, params, predict.mixgpd_fit, or the causal estimand helpers.

Setting GPD = TRUE requests the spliced bulk-tail model with conditional distribution $$F(y \mid x) = F_{\mathrm{bulk}}(y \mid x)\mathbf{1}\{y \le u(x)\} + \left[p_u(x) + \{1 - p_u(x)\}F_{\mathrm{GPD}}(y \mid x)\right]\mathbf{1}\{y > u(x)\},$$ where \(p_u(x)\) is the bulk probability below the threshold \(u(x)\).

See the manuscript vignette for the DPM hierarchy, SB/CRP representations, and the spliced bulk-tail construction used throughout the package.

See also

build_nimble_bundle, build_causal_bundle, mcmc, dpmix, dpmgpd.