---
title: "Supported models"
description: >
  The model classes `table_regression()` supports, in one table:
  linear and generalized linear models, robust and IV estimators,
  mixed effects, ordinal and multinomial outcomes, two-part count
  models, survival models, survey-weighted and Bayesian fits -- with
  each family's marginal-effects estimand, exponentiate semantics,
  robust-variance backends, and standardized-coefficient support.
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{Supported models}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(width = 110)
```

```{r setup, message = FALSE}
library(spicy)
```

`table_regression()` takes fitted model objects -- never raw data plus
a formula -- and renders them as publication-ready coefficient tables.
"Supported" is a commitment, not a list of classes that happen to run:
every number a supported class produces is validated against a field
reference (the model's own `summary()`, `sandwich`, `clubSandwich`,
`marginaleffects`, `effectsize`, `performance`, Stata or SPSS
conventions), and every request a class cannot honour is **refused
with a classed error** that names what is available -- never rendered
as a silently empty or approximate column.

This article is the map. Each family below links to a dedicated
article that walks through its behaviour in depth.

## The registry

The table is generated from the same internal registry that the
package itself uses, so it cannot drift from the code. Call
`table_regression_models()` to get it as a data frame.

```{r registry, echo = FALSE}
reg <- table_regression_models()
reg$family[duplicated(reg$family)] <- ""
reg$class <- paste0("`", reg$class, "`")
reg$engine <- vapply(
  strsplit(reg$engine, ", ", fixed = TRUE),
  function(fns) paste0("`", fns, "`", collapse = ", "),
  character(1)
)
knitr::kable(
  reg,
  col.names = c("Family", "Class", "Engine", "AME", "Exponentiate",
                "Blocks")
)
```

How to read the columns:

* **AME** -- what `show_columns = c("b", "ame")` adds. `yes` is an
  average marginal effect on the response scale (a probability or
  rate effect for GLM families, the slope itself under identity).
  `per category` (ordinal) is the effect on each P(Y = k);
  `per outcome` (multinomial) is one effect per non-reference
  outcome. `survival::coxph` refuses AME -- the hazard scale has no
  marginal-probability effect -- and provides covariate-adjusted
  `rmst` and `risk_diff` columns instead (also available for
  `survreg`; `rms::cph` and `flexsurvreg` support neither AME nor
  the estimand columns). `yes (draws)` means the
  effect is computed per posterior draw and summarized as a
  posterior median with MAD SD and credible interval.
* **Exponentiate** -- the labelled ratio `exponentiate = TRUE`
  produces. The label follows the link: OR under logit, IRR for
  count log-links, RR for the binomial log link, MR (mean ratio)
  for Gamma log links, HR for proportional hazards, TR (time ratio)
  for accelerated-failure-time models. Identity-link fits warn and
  stay untouched; links whose exponential is not a ratio (probit,
  cauchit, inverse) are refused.
* **Blocks** -- labelled subordinate row blocks rendered inside the
  same table (random effects, thresholds, zero components,
  per-outcome segments), each explained by a footer line.

## Cross-cutting arguments

The same arguments work across families, each through the family's
field-standard backend -- or a clear refusal.

**Robust and cluster-robust standard errors** (`vcov`, `cluster`).
`lm` and `glm` take `HC0`--`HC5` (`sandwich`) and cluster-robust
`CR0`--`CR3` (`clubSandwich`, bias-reduced with Satterthwaite df),
plus `"bootstrap"` / `"jackknife"` resampling estimators. Among the
mixed engines, `lmer`, `nlme::lme` and `glmmTMB` take `CR*` via
`clubSandwich` (for `glmmTMB` the sandwich covers the conditional
part only, and the footer says so); `glmer` and `gls` are
model-based only -- `clubSandwich` has no working backend for them.
Ordinal models take `CR0`--`CR3` but no `HC*`, and the cut-point
thresholds are reweighted from the same clustered vcov (a `clm`
with a scale or nominal partial-proportional-odds component is
model-based only). `multinom` takes `CR*` (one cluster value per
observation) and `mlogit` takes `CR*` (one per choice situation) --
both refuse `HC*`, which has no valid working-residual form for
multi-equation models. Quantile regression (`rq`) uses its own
estimator family -- `"classical"` resolves to the robust `nid`
sandwich, `iid` / `ker` / `rank` are opt-ins, and clustering goes
through its native wild gradient bootstrap (`vcov = "bootstrap"` +
`cluster`; `HC*` / `CR*` are refused). Cox models use the Lin-Wei
grouped-dfbeta
sandwich, and the `rms` fits take `CR*` via `rms::robcov()` (refit
with `x = TRUE, y = TRUE`); `survreg`, `gam` / `bam` and `betareg`
take `CR*` via `sandwich::vcovCL()`; `pscl` two-part fits cluster
both components. `estimatr` fits keep their own robust SEs, and `fixest`
fits keep the estimator they were computed with (the footer carries
fixest's own label -- IID, clustered, Newey-West, ... -- and
spicy's `HC*` / `CR*` tokens are refused for them);
`svyglm` is design-based by default and additionally accepts
design-aware `CR0`--`CR3`. Bayesian fits refuse `vcov` -- nothing
standard plays the sandwich role for a posterior. Whatever the
backend, the footer names the estimator actually applied, and a
robust vcov also flows into the AME uncertainty.

**Standardized coefficients** (`standardized`). Available for `lm`,
`glm` (including `MASS::glm.nb`), the mixed engines (`lmer` /
`glmer` / `glmmTMB` / `nlme::lme`), and fixed-effects Bayesian fits
-- `stan_glm`-style models and standard-formula `brm()` models --
where `"posthoc"`, `"basic"` and `"smart"` are exact affine
rescales of the posterior draws. Other classes -- including
multilevel Bayesian fits and brms formulas with distributional or
special terms -- refuse with a hint to standardize predictors
before fitting.

**Confidence intervals** (`ci_method`). Wald everywhere by default;
`"profile"` (profile likelihood) for `glm`, `polr` and `clm`;
`"boot_percentile"` (with `vcov = "bootstrap"`) replaces the bounds
with equal-tailed percentile intervals of the bootstrap replicates;
`"hdi"` (highest-density interval) for Bayesian fits, which
otherwise report equal-tailed credible intervals.

**Model comparison and multiplicity.** `nested = TRUE` compares
nested fits by the family's change-test convention: Delta R-squared
with the partial F test for `lm`, the likelihood-ratio test for
`glm`, mixed, `multinom` and Cox models, and `anova.rq`'s Wald-type
F for quantile regressions (all fits at one tau). `p_adjust` applies a
multiplicity correction across the displayed p-values -- and is
refused for Bayesian tables, which carry no p-values at all.

## The families in brief

**Linear and generalized linear.** The core engines: `lm`, `glm`
(with profile CIs on request), `MASS::glm.nb` (with opt-in `theta` /
`alpha` dispersion statistics), `MASS::rlm`, `stats::nls`. Start
with `vignette("table-regression")` and
`vignette("categorical-predictors")`.

**Robust, IV, quantile, panel.** `estimatr::lm_robust()` /
`iv_robust()`, `AER::ivreg()` and `AER::tobit()`, `quantreg::rq()`
(defaulting to the heteroskedasticity-robust `nid` sandwich --
quantreg's own large-sample default -- with `iid`, `ker`, `rank`
CIs and a native clustered bootstrap as `vcov` options), and the
`fixest` estimators, whose absorbed fixed effects render as a
default-on `Fixed effects:` block -- one Yes / No row per factor,
blank for non-fixest models in a mixed table -- with the within
R-squared among the default fit statistics and per-factor
`N (<factor>)` counts through the opt-in `"n_groups"` token.

**Mixed effects.** `lmer` (Satterthwaite t via `lmerTest`), `glmer`,
`glmmTMB` (with zero-inflation and dispersion blocks), `nlme::lme`
and `nlme::gls`. Random effects render as rows -- SD, correlations,
residual -- deliberately without per-row p-values; the footer
carries the boundary-correct chi-bar-squared test, and
`re_test = "lrt"` / `"rlrt"` adds per-term tests. See
`vignette("table-regression-mixed")`.

**Ordinal.** `MASS::polr` and `ordinal::clm`: proportional odds
ratios, a `Thresholds` block for the cut-points (log-odds scale,
never exponentiated), partial-proportional-odds terms as a
`Non-proportional effects` block, and per-category AME. See
`vignette("table-regression-ordinal")`.

**Categorical.** `nnet::multinom` renders outcome categories as
columns with per-outcome AME; `mlogit::mlogit` renders
per-alternative rows for discrete-choice designs. See
`vignette("table-regression-multinomial")`.

**Counts and two-part.** Poisson and negative binomial through
`glm` / `glm.nb` / `glmmTMB`, plus `pscl::zeroinfl()` and
`pscl::hurdle()` with their zero components as labelled blocks and a
combined-response AME. See `vignette("table-regression-counts")`.

**Survival.** `survival::coxph` and `rms::cph` (hazard ratios,
`strata()` supported), `survival::survreg` (time ratios) and
`flexsurv::flexsurvreg`. Absolute effects come as covariate-adjusted
RMST and risk differences by g-computation for `coxph` and
`survreg` fits. See `vignette("table-regression-survival")`.

**Survey-weighted.** `survey::svyglm()`: design-based inference,
weighted and unweighted n.

**Additive, proportions, selection.** `mgcv::gam()` / `bam()`,
`betareg::betareg()` (odds ratios on the logit mean link under
`exponentiate`; the precision is the opt-in `phi` fit statistic),
and `sampleSelection::selection()` with its selection component as
a block.

**rms.** `ols`, `lrm`, `Glm` and `cph` are first-class citizens, so
Harrell-style workflows drop in directly.

**Bayesian.** `rstanarm` and `brms` fits are summarized from their
posterior draws: posterior median, MAD SD, credible intervals,
draws-native exponentiation and AME, sampler diagnostics checked on
every fit (with opt-in `pd`, `rhat`, `ess_bulk` / `ess_tail` and
`mcse` columns). No p-values, by design. See
`vignette("table-regression-bayesian")`.

## When a class is not supported

An unsupported class fails fast with a classed error
(`spicy_unsupported`):

```{r refusal, error = TRUE}
fit <- loess(dist ~ speed, data = cars)
table_regression(fit)
```

The same contract applies inside a family: a request a class cannot
honour -- `HC*` for `multinom`, AME for `mlogit`, `exponentiate` on
a probit link, `p_adjust` on a Bayesian table -- is refused with the
reason and the supported alternative, never silently degraded.

## Programmatic access

`table_regression_models()` returns the registry as a plain data
frame -- convenient to filter, join, or cite:

```{r accessor}
subset(table_regression_models(), family == "Survival")
```

The per-family reference sections live on its help page:
`?table_regression_models` (also reachable as
`?table_regression_mixed`, `?table_regression_ordinal`,
`?table_regression_survival`, and the other family aliases).