# Chapter A08: Overview of Envelope Related Functions

#### Kjell Nygren

#### 2026-08-02

    library(glmbayes)

# Chapter A08: Overview of Envelope Related Functions

## 1. Introduction and Purpose

The `glmbayes` package produces independent and identically distributed (iid) posterior samples for Bayesian generalized linear models. When the posterior is not available in closed form (e.g., Poisson or binomial regression with Normal priors, Gaussian regression with independent Normal-Gamma priors), the package uses accept-reject sampling based on tight enveloping functions. These envelopes are constructed using the likelihood subgradient approach of (Nygren and Nygren 2006).

This chapter provides a central overview of the envelope-related functions in the package. It consolidates the theoretical foundations, the function map, and the workflow so that users and developers can see how the pieces fit together. For implementation details of the full simulation pipeline, see Chapter A05. For envelope theory in specialized settings, see Chapter A06 (dispersion envelopes) and Chapter A07 (joint envelopes for independent Normal-Gamma priors).

### 1.1 Why Envelopes?

Accept-reject sampling requires a proposal density \(q(\theta)\) and a constant \(a\) such that \(a \cdot q(\theta) \geq \pi(\theta \mid y)\) for all \(\theta\). The posterior \(\pi(\theta \mid y)\) is then sampled by: draw \(\theta \sim q\), accept with probability \(\pi(\theta \mid y) / (a \cdot q(\theta))\). The constant \(a\) controls efficiency: smaller \(a\) means fewer candidates per acceptance. The likelihood subgradient approach constructs tight envelopes, with \(a\) bounded above by \((2/\sqrt{\pi})^k\) for \(k\)-dimensional models when the likelihood is approximately normal (Nygren & Nygren 2006, Theorems 2 and 3). This ensures efficient iid sampling even in high dimensions.

### 1.2 High-Level Flow

The envelope-based pipeline follows this sequence:

1. Optimize - Find the posterior mode.
2. Standardize - Reparameterize into standard form (prior precision = identity, posterior precision diagonal at the mode).
3. Size - Choose the number of tangency points per dimension (`EnvelopeSize`, `EnvelopeOpt`).
4. Build - Construct the envelope (`EnvelopeBuild`).
5. Sort - Order envelope components for sampling efficiency (`EnvelopeSort`).
6. Sample - Generate candidates from the mixture envelope and accept/reject (`rNormalGLM_std` or C++ equivalents).

For models with unknown dispersion, an additional dispersion envelope step (`EnvelopeDispersionBuild`) extends the coefficient envelope to a joint envelope over \((\beta, \phi)\). This is orchestrated by `EnvelopeOrchestrator` for the independent Normal-Gamma case.

## 2. Theoretical Foundation

The envelope construction is grounded in Nygren & Nygren (2006), “Likelihood Subgradient Densities,” Journal of the American Statistical Association. This section records the definitions and structural results from the paper that justify mixture envelopes and the \(3^p\) partition. For how these are implemented in glmbayes, see Chapter A05.

### 2.1 Posterior and Definition 2 (paper Section 2)

Let the posterior be \[ \pi(\theta \mid y) \propto f(y \mid \theta)\,\pi(\theta), \] where \(f(y \mid \theta)\) is the likelihood and \(\pi(\theta)\) is the prior density.

Definition 2. A density \(q(\cdot)\) is a generalized likelihood-subgradient density for \(\pi(\cdot\mid y)\) with prior \(\pi(\cdot)\) and likelihood \(f(y\mid\cdot)\) at \(\bar{\theta}\in\Theta\) if there exist a function \(g\) and a subgradient \(c(\bar{\theta})\) of \(-\log g\) at \(\bar{\theta}\) such that:

1. (a) \(g(\cdot)\) bounds \(f(y\mid\cdot)\) from above;
2. (b) \(\mathrm{MGF}(-c(\bar{\theta})) = \int_{\Theta} \exp(-c(\bar{\theta})^T\theta)\,\pi(\theta)\,d\theta\) is finite;
3. (c) for all \(\theta\in\Theta\), \[ q(\theta) = \frac{\exp\!\big(-c(\bar{\theta})^T\theta\big)\,\pi(\theta)} {\mathrm{MGF}\!\big(-c(\bar{\theta})\big)}. \]

Special cases (paper). If \(g\) is the likelihood, \(q\) is a likelihood-subgradient density; log-concave likelihoods (e.g., Poisson and logistic regression) are the main examples. If \(g\) is a constant upper bound on the likelihood, one obtains the Bayesian construction where the prior itself is viewed as a generalized likelihood-subgradient density.

Existence (paper Appendix). Likelihood-subgradient densities exist at every \(\bar{\theta}\) when the prior is a finite mixture of multivariate normals and the likelihood is log-concave. Mixture priors yield particularly simple algebraic forms (Claim 1 below).

### 2.2 Claim 1: finite mixture of normal priors

Suppose \(\pi(\theta) = \sum_{i=1}^{k} p_i\,\pi_i(\theta\mid \mu_i,\Sigma_i)\) and \(c(\bar{\theta})\) is a subgradient for \(-\log g\) at \(\bar{\theta}\). Then \[ \mathrm{MGF}\!\big(-c(\bar{\theta})\big) = \sum_{i=1}^{k} p_i\, \exp\!\Big( -c(\bar{\theta})^T\mu_i + \tfrac12\,c(\bar{\theta})^T\Sigma_i\,c(\bar{\theta}) \Big), \] and the generalized likelihood-subgradient density is again a mixture of multivariate normals: \[ q(\theta) = \sum_{i=1}^{k} \tilde{p}_i\,\pi_i(\theta\mid \tilde{\mu}_i,\Sigma_i), \] with \[ \tilde{p}_i = \frac{ p_i\,\exp\!\big(-c(\bar{\theta})^T\mu_i + \tfrac12\,c(\bar{\theta})^T\Sigma_i\,c(\bar{\theta})\big) }{ \mathrm{MGF}\!\big(-c(\bar{\theta})\big) }, \qquad \tilde{\mu}_i = \mu_i - \Sigma_i\,c(\bar{\theta}). \]

### 2.3 Theorem 1: envelope dominance and tangency

Define \[ a(\bar{\theta}) = \frac{ g(\bar{\theta})\,\mathrm{MGF}\!\big(-c(\bar{\theta})\big) }{ f(y)\,\exp\!\big(-c(\bar{\theta})^T\bar{\theta}\big) }, \] and \[ h_{\bar{\theta}}(\theta) = \frac{ \exp\!\big(-c(\bar{\theta})^T\bar{\theta}\big)\,f(y\mid\theta) }{ \exp\!\big(-c(\bar{\theta})^T\theta\big)\,g(\bar{\theta}) }. \]

Then Theorem 1 states \[ a(\bar{\theta})\,q_{\bar{\theta}}(\theta) \ge a(\bar{\theta})\,h_{\bar{\theta}}(\theta)\,q_{\bar{\theta}}(\theta) = \pi(\theta\mid y), \] with \[ 0 \le h_{\bar{\theta}}(\theta) \le 1 \quad \forall \theta\in\Theta. \]

If \(f(y\mid\bar{\theta}) = g(\bar{\theta})\), then \(h_{\bar{\theta}}(\bar{\theta})=1\), so the envelope touches the posterior at tangency.

Operationally, \(a(\bar{\theta})\) governs acceptance efficiency: the acceptance probability is \(1/a(\bar{\theta})\).

### 2.4 Models in standard form

Following Nygren & Nygren (2006, Section 3.3), the envelope construction assumes the model has been reparameterized into standard form. In this form:

- The prior precision matrix is the identity.
- The posterior precision matrix, evaluated at the posterior mode \(\theta^\star\), is diagonal.

Example 2 in the paper illustrates the special case of a zero-mean normal prior with identity covariance. In this setting:

- The generalized likelihood-subgradient density at a tangency point \(\bar{\theta}\) has mean vector \(-c(\bar{\theta})\) and covariance \(I\).

- The normalizing integrals over restricted sets factorize across dimensions. Specifically, for a rectangular set \(A = \{\theta : l_L \le \theta \le l_U\}\), we have

  \[ \int_{\theta \in A} q^{\bar{\theta}}(\theta)\, d\theta \;=\; \prod_{r=1}^{p} \Big[ \Phi\!\big(l_{U,r} + c_{r}(\bar{\theta})\big) - \Phi\!\big(l_{L,r} + c_{r}(\bar{\theta})\big) \Big], \]

  and the truncated expectation in coordinate \(r\) is

  \[ \mathbb{E}_{\tilde{q}^{\bar{\theta}}}[\theta_r \mid \theta \in A] = -\,c_{r}(\bar{\theta}) + \frac{ \phi\!\big(l_{L,r} + c_{r}(\bar{\theta})\big) - \phi\!\big(l_{U,r} + c_{r}(\bar{\theta})\big) }{ \Phi\!\big(l_{U,r} + c_{r}(\bar{\theta})\big) - \Phi\!\big(l_{L,r} + c_{r}(\bar{\theta})\big) }. \]

These closed-form expressions explain why `EnvelopeBuild` evaluates `logU`, `loglt`, and `logrt` using univariate normal CDFs and densities, rather than numerical integration. The gradients (`cbars`) directly determine the shifted means of the restricted densities, and the separability across dimensions makes the grid-based construction computationally tractable.

Models with Zellner’s \(g\)-priors are essentially in standard form, since in the whitened design space both the prior and likelihood precisions are diagonal. Each dimension still needs to be scaled so that the prior precision is exactly the identity matrix. For other models, standard form can be achieved by reparameterization (e.g. via Cholesky of the posterior precision) or by shifting part of the prior quadratic form into the likelihood.

### 2.5 Construction of restricted subgradient densities

Following Remark 5 in Nygren & Nygren (2006), each unrestricted likelihood-subgradient density \(q_{\bar{\theta}}(\cdot)\) can be restricted to a subset \(A \subset \Theta\). The restricted density is defined as

\[ \tilde{q}_{\bar{\theta}}(\theta) = \frac{ q_{\bar{\theta}}(\theta)\,\mathbf{1}_{\{\theta \in A\}} } { \int_{\theta' \in A} q_{\bar{\theta}}(\theta')\, d\theta' }, \] and the corresponding constant is

\[ \tilde{a}(\theta_{\text{bar}}) = a(\theta_{\text{bar}})\, \int_{\theta \in A} q_{\theta_{\text{bar}}}(\theta)\, d\theta, \] where \(a(\theta_{\text{bar}})\) is the global normalizing constant from Theorem 1. For every \(\theta \in A\), the identity

\[ \tilde{a}(\theta_{\text{bar}})\, h_{\theta_{\text{bar}}}(\theta)\, \tilde{q}_{\theta_{\text{bar}}}(\theta) = \pi(\theta \mid y) \] holds, ensuring that the restricted densities reproduce the posterior when combined.

The constant \(a(\bar{\theta})\) is defined in Theorem 1 as

\[ a(\bar{\theta}) = \frac{g(\bar{\theta})\,\mathrm{MGF}\!\big(-c(\bar{\theta})\big)} {f(y)\,\exp\!\big(-c(\bar{\theta})^{T}\bar{\theta}\big)}, \] where \(g(\bar{\theta})\) is the reference density at the tangency point, \(c(\bar{\theta})\) is the subgradient of the log-likelihood, and \(\mathrm{MGF}(-c(\bar{\theta}))\) is the moment-generating function of the prior evaluated at \(-c(\bar{\theta})\).

The envelope function \(h_{\bar{\theta}}(\theta)\) is defined as

\[ h_{\bar{\theta}}(\theta) = \frac{\exp\!\big(-c(\bar{\theta})^{T}\bar{\theta}\big)\,f(y\mid\theta)} {\exp\!\big(-c(\bar{\theta})^{T}\theta\big)\,g(\bar{\theta})}, \] and satisfies

\[ 0 \le h_{\bar{\theta}}(\theta) \le 1 \quad \forall\,\theta \in \Theta, \qquad h_{\bar{\theta}}(\bar{\theta}) = 1 \quad \text{if } f(y\mid\bar{\theta}) = g(\bar{\theta}). \]

In the standardized model (zero-mean normal prior with identity covariance and diagonal posterior precision at the mode), the restricted integral \(\int_{A} q_{\bar{\theta}}(\theta)\, d\theta\) factorizes across dimensions and can be evaluated in closed form using normal CDFs:

\[ \int_{\theta \in A} q_{\bar{\theta}}(\theta)\, d\theta = \prod_{r=1}^{p} \Big[ \Phi\!\big(l_{U,r} + c_{r}(\bar{\theta})\big) - \Phi\!\big(l_{L,r} + c_{r}(\bar{\theta})\big) \Big]. \]

In standardized models, where the prior is \(\mathcal{N}(0, I)\), the moment-generating function simplifies to

\[ \mathrm{MGF}\big(-c(\bar{\theta})\big) = \exp\left(\tfrac{1}{2} c(\bar{\theta})^{T} c(\bar{\theta})\right), \] so the constant \(a(\bar{\theta})\) from Theorem 1 becomes

\[ a(\bar{\theta}) = \frac{g(\bar{\theta})\,\exp\left(\tfrac{1}{2} c(\bar{\theta})^{T} c(\bar{\theta})\right)} {f(y)\,\exp\!\big(-c(\bar{\theta})^{T} \bar{\theta}\big)}. \]

This is why `EnvelopeBuild` computes and stores `logU`, `loglt`, and `logrt` using univariate normal CDF evaluations. The constants \(\tilde{a}(\bar{\theta})\) are then obtained by scaling the global constant with these integrals, and the mixture weights (`PLSD`) are normalized accordingly. In practice:

- `EnvelopeSet_Grid_C2_pointwise` evaluates restricted densities at each grid point.
- `LLconst` stores the log of the restricted integrals.
- `EnvelopeSet_LogP_C2` computes \(\tilde{a}(\bar{\theta})\) and normalizes mixture weights.

### 2.6 Mixture construction and tractable probabilities

Claim 2 in Nygren & Nygren (2006) shows that the posterior density \(\pi(\theta \mid y)\) can be expressed as a mixture of restricted likelihood-subgradient densities. Let \(A_1, \dots, A_m\) be a partition of the parameter space \(\Theta\), and define

\[ \tilde{q}_{\bar{\theta}}(\theta) = \sum_{i=1}^{m} \tilde{p}_i \, q^{\bar{\theta}}_{A_i}(\theta), \qquad \tilde{p}_i = \frac{ \tilde{a}_i }{ \sum_{j=1}^{k} \tilde{a}_j }. \]

In Remark 6 of Nygren & Nygren (2006), the mixture weights \(\tilde{p}_i\) for each restricted likelihood-subgradient density \(q^{\bar{\theta}_i}_{A_i}\) are defined as

\[ \tilde{p}_i = \frac{ g(\bar{\theta}_i)\,\mathrm{MGF}\!\left(-c(\bar{\theta}_i)\right)\, \dfrac{ \int_{\theta \in A_i} q^{{\bar{\theta}_i}}(\theta)\, d\theta }{ \exp\!\left(-c(\bar{\theta}_i)^{\top}\bar{\theta}_i\right) } }{ \displaystyle\sum_{j=1}^{k} g(\bar{\theta}_j)\,\mathrm{MGF}\!\left(-c(\bar{\theta}_j)\right)\, \dfrac{ \int_{\theta \in A_j} q^{{\bar{\theta}_j}}(\theta)\, d\theta }{ \exp\!\left(-c(\bar{\theta}_j)^{\top}\bar{\theta}_j\right) } }. \]

This expression reflects the full normalization of the mixture, where each \(\tilde{p}_i\) is proportional to the restricted constant \(\tilde{a}_i(\bar{\theta}_i)\) from Remark 5, and the denominator sums over all such constants across the partition. The resulting mixture \(\tilde{q}^{\bar{\theta}}(\theta)\) is a valid approximation to the posterior density \(\pi(\theta \mid y)\).

Remark 6 emphasizes that these mixture weights are tractable to compute in standardized models. When the prior is \(\mathcal{N}(0, I)\) and the posterior precision is diagonal at the mode, each integral \(\int_{A_i} q^{\bar{\theta}}(\theta)\, d\theta\) factorizes across dimensions and can be evaluated using normal CDFs:

\[ \int_{\theta\in A_i} q^{{\bar{\theta}}}(\theta)\, d\theta = \displaystyle\prod_{r=1}^{p} \Bigl[ \Phi\!\bigl(l_{U,r}^{(i)} + c_{r}(\bar{\theta})\bigr) - \Phi\!\bigl(l_{L,r}^{(i)} + c_{r}(\bar{\theta})\bigr) \Bigr]. \]

where \(l_{L}^{(i)}\) and \(l_{U}^{(i)}\) are the bounds defining region \(A_i\).

This tractability is central to the envelope construction. It allows `EnvelopeBuild` to compute:

- `LLconst`, which stores the log of each restricted integral \(\log \int_{A_i} q^{\bar{\theta}}(\theta)\, d\theta\).
- `PLSD`, which stores the normalized mixture weights \(\tilde{p}_i\).

These quantities are used to construct the combined density \(\tilde{q}_{\bar{\theta}}(\theta)\) and to evaluate the envelope approximation to the posterior. Because all components are normalized and tractable, the mixture is both valid and computationally efficient.

### 2.7 Log-scale properties of the envelope function

The envelope function \(h_{\bar{\theta}}(\theta)\) is defined in Theorem 1 as

\[ h_{\bar{\theta}}(\theta) = \frac{\exp\!\big(-c(\bar{\theta})^{T} \bar{\theta}\big)\,f(y \mid \theta)} {\exp\!\big(-c(\bar{\theta})^{T} \theta\big)\,g(\bar{\theta})}. \]

When \(g(\bar{\theta}) = f(y \mid \bar{\theta})\), this simplifies to

\[ h_{\bar{\theta}}(\theta) = \exp\!\big( c(\bar{\theta})^{T}(\theta - \bar{\theta}) \big) \cdot \frac{f(y \mid \theta)}{f(y \mid \bar{\theta})}. \]

Taking logarithms yields

\[ \log h_{\bar{\theta}}(\theta) = c(\bar{\theta})^{T}(\theta - \bar{\theta}) + \log f(y \mid \theta) - \log f(y \mid \bar{\theta}), \]

which is tractable as long as the log-likelihood \(\log f(y \mid \theta)\) is. In particular, if the log-likelihood is concave or piecewise affine, then \(\log h_{\bar{\theta}}(\theta)\) inherits that structure and can be efficiently evaluated across grid regions.

This tractability is central to the envelope construction: it allows `EnvelopeBuild` to evaluate the envelope function pointwise using `EnvelopeSet_Grid_C2_pointwise`, and ensures that the resulting approximation remains bounded between 0 and 1. At the tangency point, we recover

\[ h_{\bar{\theta}}(\bar{\theta}) = 1, \] confirming that the envelope touches the posterior density exactly.

The key inequality that ensures envelope dominance follows from the subgradient inequality for concave functions. If \(\log f(y \mid \theta)\) is concave and \(c(\bar{\theta})\) is a subgradient at \(\bar{\theta}\), then

\[ \log f(y \mid \theta) \le \log f(y \mid \bar{\theta}) + c(\bar{\theta})^{T}(\theta - \bar{\theta}), \]

which implies

\[ \log h_{\bar{\theta}}(\theta) \le 0, \qquad h_{\bar{\theta}}(\theta) \le 1. \]

This inequality guarantees that the envelope function dominates the posterior density pointwise, as required by Theorem 1. Equality holds at the tangency point \(\theta = \bar{\theta}\), where

\[ h_{\bar{\theta}}(\bar{\theta}) = 1. \]

### 2.8 Standard form: Definition 3 and Remarks 11-15 (paper Section 3.3)

Remark 11. If the log-likelihood is concave and twice continuously differentiable, a Cholesky reparameterization from the posterior precision at the unique mode achieves diagonal posterior Hessian at the mode.

Remark 12. For any positive definite prior precision \(P\), there exists diagonal \(D\succ 0\) such that \(P-D\succ 0\).

Remark 13. With \(P\) and \(D\) as in Remark 12, shifting \((P-D)\) from the prior quadratic form into the likelihood leaves the posterior unchanged: the same \(\pi(\theta\mid y)\) arises from prior precision \(D\) and an augmented log-likelihood.

Remark 14. If the prior covariance is diagonal and the posterior Hessian at the mode is diagonal, the model can be reparameterized into standard form.

Remark 15. Any model with Normal prior and log-concave, twice differentiable log-likelihood can be reparameterized into standard form.

### 2.10 Remarks 7-8: sampling restricted normals (paper)

Remark 7. Sampling from restricted Normal laws can be done by inverse transform (Fishman 1999).

Remark 8. When intervals lie in extreme Gaussian tails, accurate evaluation of normal CDFs (or their logs) with uniformly small relative error matters; standard approximation theory and tables are discussed in (Hart et al. 1968); normal distribution properties relevant to tail behavior appear in (Bryc 2002).

### 2.11 Theorem 2: univariate Normal data, three intervals, and \(\tilde{a}\to 2/\sqrt{\pi}\)

Empirically (paper), a single optimally placed likelihood-subgradient component, and two-interval partitions, can become loose as sample size grows; the three-interval construction does not exhibit the same deterioration.

Let \(\theta^\ast\) be the posterior mode. Define (univariate case) \[ \omega = \frac{ \sqrt{2} - \exp\!\big(-1.20491 - 0.7321\,(0.5 - \partial^2\log f(\theta^\ast\mid y)/\partial\theta^2)\big) }{ 1 - \partial^2\log f(\theta^\ast\mid y)/\partial\theta^2 }, \] \[ \ell_1=\theta^\ast-0.5\,\omega,\quad \ell_2=\theta^\ast+0.5\,\omega, \] regions \(A_1=(-\infty,\ell_1)\), \(A_2=[\ell_1,\ell_2]\), \(A_3=(\ell_2,\infty)\), and tangencies \(\bar{\theta}_1=\theta^\ast-\omega\), \(\bar{\theta}_2=\theta^\ast\), \(\bar{\theta}_3=\theta^\ast+\omega\).

For \(N\) i.i.d. Normal\((\theta,1)\) observations, \(-\partial^2\log f(\theta^\ast\mid y)/\partial\theta^2 = N\).

Theorem 2. With \(\tilde{a}^\ast(N)\) the combined constant at sample size \(N\), \[ \lim_{N\to\infty} \tilde{a}^\ast(N) = \frac{2}{\sqrt{\pi}}. \] The paper gives a proof sketch using symmetry \(\tilde{a}_1(\bar{\theta}_1)=\tilde{a}_3(\bar{\theta}_3)\) and appendix claims.

### 2.12 Multivariate partition in standard form (paper; Remark 16)

Let \(\theta^\ast\) be the unique posterior mode. For each coordinate \(i\), define \[ \omega_i = \frac{ \sqrt{2} - \exp\!\big(-1.20491 - 0.7321\,(0.5 - \partial^2\log f(\theta^\ast\mid y)/\partial\theta_i^2)\big) }{ 1 - \partial^2\log f(\theta^\ast\mid y)/\partial\theta_i^2 }, \] then \(\ell_{i,1}=\theta^\ast_i-0.5\,\omega_i\), \(\ell_{i,2}=\theta^\ast_i+0.5\,\omega_i\), and \[ A_{i,1}=(-\infty,\ell_{i,1}),\quad A_{i,2}=[\ell_{i,1},\ell_{i,2}],\quad A_{i,3}=(\ell_{i,2},\infty). \] Let \(j=(j_1,\ldots,j_p)\in\{1,2,3\}^p\) and \(A^\ast_j=\prod_{i=1}^p A_{i,j_i}\). The \(A^\ast_j\) partition \(\mathbb{R}^p\).

With \(C_{j1}=\{i:j_i=1\}\), \(C_{j2}=\{i:j_i=2\}\), \(C_{j3}=\{i:j_i=3\}\), tangency \(\bar{\theta}_j\) has coordinates \(\theta^\ast_i-\omega_i\), \(\theta^\ast_i\), or \(\theta^\ast_i+\omega_i\) according as \(i\) lies in \(C_{j1}\), \(C_{j2}\), or \(C_{j3}\).

Remark 16. The resulting mixture generalized likelihood-subgradient density is a mixture of restricted multivariate Normal laws for which a direct sampling procedure exists.

### 2.13 Theorem 3 (paper)

For \(k\)-dimensional Normal regression in standard form, the three-interval construction yields \[ \tilde{a}\le \left(\frac{2}{\sqrt{\pi}}\right)^k. \]

For other log-concave models, posterior asymptotic normality suggests analogous practical tightness as the posterior concentrates.

## 3. Function Map and Workflow

### 3.1 Where Envelopes Are Used

Simfunction | When | Envelope role
--- | --- | ---
`rNormal_reg` | Non-Gaussian families (Poisson, binomial, Gamma, etc.) with Normal prior | `EnvelopeBuild` for coefficients
`rindepNormalGamma_reg` | Gaussian with independent Normal-Gamma prior | `EnvelopeOrchestrator` -> `EnvelopeBuild` + `EnvelopeDispersionBuild`
`rGamma_reg` | Gamma family with Gamma prior on dispersion | Dispersion envelope (see Chapter A06)

For conjugate cases (e.g., Gaussian likelihood with Normal or Normal-Gamma prior), no envelope is constructed; direct sampling is used.

### 3.2 Pipeline Order

The following functions are invoked in sequence during envelope-based sampling:

1. `glmb_Standardize_Model` - Transforms the model into standard form. Required before `EnvelopeBuild`.

2. `EnvelopeSize` - Determines the grid structure (number of tangency points per dimension). Calls `EnvelopeOpt` when `Gridtype = 2`.

3. `EnvelopeBuild` - Constructs the envelope: tangency points, gradients, mixture weights, and sampling constants. This is the main envelope construction function.

4. `EnvelopeSort` - Reorders envelope components by probability mass to improve sampling efficiency.

5. `EnvelopeSetGrid`, `EnvelopeSetLogP` - Low-level helpers that compute log-densities and constants for the envelope. Typically called internally by `EnvelopeBuild` or the C++ routines.

6. `rNormalGLM_std` - Samples from the posterior using the constructed envelope via accept-reject. Used for non-Gaussian GLMs.

7. `EnvelopeDispersionBuild` - For Gaussian regression with independent Normal-Gamma priors, refines the coefficient envelope to a joint envelope over \((\beta, \phi)\). Called by `EnvelopeOrchestrator`.

8. `EnvelopeOrchestrator` - High-level coordinator that runs `EnvelopeBuild`, `EnvelopeDispersionBuild`, and `EnvelopeSort` for the independent Normal-Gamma case. Used inside the C++ sampler.

## 4. Envelope Functions Reference

Function | Purpose | Typically called by | Theory chapter
--- | --- | --- | ---
`glmb_Standardize_Model` | Reparameterize to standard form | `.rNormalGLM_cpp`, C++ routines | A05
`EnvelopeSize` | Choose grid size per dimension | `EnvelopeBuild` (or C++ equivalent) | A05, A08
`EnvelopeOpt` | Adaptive grid optimization (Gridtype 2) | `EnvelopeSize` | A05
`EnvelopeBuild` | Build coefficient envelope | `rNormal_reg`, `EnvelopeOrchestrator` | A05, A08
`EnvelopeSort` | Sort envelope components | `EnvelopeBuild`, `EnvelopeOrchestrator` | A08
`EnvelopeSetGrid` | Compute grid log-densities | `EnvelopeBuild` (internal) | -
`EnvelopeSetLogP` | Compute mixture constants | `EnvelopeBuild` (internal) | -
`EnvelopeDispersionBuild` | Joint envelope for \((\beta, \phi)\) | `EnvelopeOrchestrator` | A07
`EnvelopeOrchestrator` | Coordinate build + dispersion + sort | `rindepNormalGamma_reg` C++ | A07, A08
`rNormalGLM_std` | Sample from standardized model | `rNormal_reg` (non-Gaussian path) | A05
`EnvelopeEval` | Evaluate envelope at points | Diagnostics, validation | -

Users typically do not call these functions directly; they are invoked by `glmb()`, `lmb()`, `rglmb()`, or `rlmb()` through the simfunctions. The `Envelope` component of the returned object (when applicable) contains the constructed envelope and can be inspected for diagnostics.

## 5. Gridtype and Envelope Sizing

The `Gridtype` argument (passed via `glmb()`, `rglmb()`, etc.) controls how many tangency points are used per dimension. This trades off envelope build cost against sampling efficiency.

Gridtype | Behavior | Expected candidates per draw
--- | --- | ---
1 | Static threshold: use single-point envelope if \(\sqrt{1+a_i} \leq 2/\sqrt{\pi}\) | \(\prod_i \sqrt{1+a_i}\) or \((2/\sqrt{\pi})^k\)
2 | Adaptive via `EnvelopeOpt`: choose per dimension | Optimized blend
3 | Always three-point envelope in every dimension | \((2/\sqrt{\pi})^k\)
4 | Always single-point at posterior mode | \(\prod_i \sqrt{1+a_i}\)

Gridtype 2 is the default. It uses `EnvelopeOpt` to balance build time (more tangents = more cost) against acceptance rate (more tangents = tighter envelope = fewer candidates). The `n_envopt` argument influences the effective sample size used in this optimization; larger values encourage tighter envelopes.

For large models, `use_opencl = TRUE` can accelerate `EnvelopeBuild` by evaluating gradients on the GPU. See Chapter A10 for OpenCL usage.

## 6. Central Distributions: Normal_ct, Gamma_ct, InvGamma_ct

The envelope-based samplers rely on restricted (truncated) distributions. Candidates are drawn from normals restricted to intervals (e.g., left tail, center, right tail) and from truncated Gamma distributions.

- `Normal_ct` (`pnorm_ct`, `rnorm_ct`): Restricted Normal CDF and random generation. Used when sampling from mixture components that correspond to intervals on each coordinate. Critical when the truncation interval is narrow, where naive evaluation can underflow.

- `Gamma_ct` (`pgamma_ct`, `rgamma_ct`, `ctrgamma`): Restricted Gamma distribution. Used in `rGamma_reg` and in dispersion sampling. Provides numerically stable evaluation and sampling under truncation.

- `InvGamma_ct` (`pinvgamma_ct`, `qinvgamma_ct`, `rinvgamma_ct`): Restricted inverse-Gamma on the dispersion scale. Used when dispersion is bounded for envelope construction.

These functions support the envelope construction and accept-reject steps; see their help pages for details.

## 7. Cross-References

- Chapter A02 - Overview of estimation procedures, mapping of families/pfamilies to simfunctions.
- Chapter A05 - Full simulation pipeline: optimization, standardization, sizing, building, sampling. Deep dive into `EnvelopeBuild` and `EnvelopeOpt`.
- Chapter A06 - Accept-reject sampling for dispersion in Gamma regression.
- Chapter A07 - Accept-reject sampling for Gaussian regression with independent Normal-Gamma priors. Joint envelope construction.
- Chapter A09 - Parallel sampling implementation (RcppParallel).
- Chapter A10 - OpenCL acceleration for `EnvelopeBuild`.
- Nygren & Nygren (2006) - Original paper: “Likelihood Subgradient Densities,” JASA.

For help on individual functions, use `?EnvelopeBuild`, `?EnvelopeSize`, `?EnvelopeOrchestrator`, etc.

# References

Bryc, Wlodzimierz. 2002. Normal Distribution: Characterizations with Applications. Springer.
Hart, John F., E. W. Cheney, Charles L. Lawson, et al. 1968. Computer Approximations. Wiley.
Nygren, K. N., and L. M. Nygren. 2006. “Likelihood Subgradient Densities.” Journal of the American Statistical Association 101 (475): 1144–56. https://doi.org/10.1198/016214506000000357.