Supported models • spicy

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.

Family	Class	Engine	AME	Exponentiate	Blocks
Linear and generalized linear	`lm`	`stats::lm()`	yes	-	-
	`glm`	`stats::glm()`	yes	OR / IRR / RR (link)	-
	`negbin`	`MASS::glm.nb()`	yes	IRR	-
	`rlm`	`MASS::rlm()`	yes	-	-
	`nls`	`stats::nls()`	no	-	-
Robust, IV, quantile, panel	`lm_robust`	`estimatr::lm_robust()`	yes	-	-
	`iv_robust`	`estimatr::iv_robust()`	yes	-	-
	`ivreg`	`AER::ivreg()`	yes	-	-
	`tobit`	`AER::tobit()`	yes	-	-
	`rq`	`quantreg::rq()`	yes	-	-
	`fixest`	`fixest::feols()`, `fixest::feglm()`, `fixest::fepois()`, `fixest::fenegbin()`	yes	`feglm`: OR / IRR	-
Mixed effects	`lmerMod`	`lme4::lmer()`	yes	-	Random effects
	`glmerMod`	`lme4::glmer()`	yes	OR / IRR (link)	Random effects
	`glmmTMB`	`glmmTMB::glmmTMB()`	yes	link-dependent (IRR for count families)	Random effects; Zero-inflation; Dispersion
	`lme`	`nlme::lme()`	yes	-	Random effects
	`gls`	`nlme::gls()`	yes	-	-
Ordinal	`polr`	`MASS::polr()`	per category	OR (logit)	Thresholds
	`clm`	`ordinal::clm()`	per category	OR (logit)	Thresholds; Non-proportional effects
Categorical	`multinom`	`nnet::multinom()`	per outcome	OR	per-outcome blocks
	`mlogit`	`mlogit::mlogit()`	no	OR	per-alternative rows
Counts, two-part	`zeroinfl`	`pscl::zeroinfl()`	yes (combined response)	IRR (count) + OR (logit zero part)	Zero-inflation
	`hurdle`	`pscl::hurdle()`	yes (combined response)	IRR (count) + OR (logit zero part)	Zero hurdle
Survival	`coxph`	`survival::coxph()`	RMST / risk diff	HR	-
	`survreg`	`survival::survreg()`	yes + RMST / risk diff	TR (log-scale distributions)	-
	`cph`	`rms::cph()`	no	HR	-
	`flexsurvreg`	`flexsurv::flexsurvreg()`	no	TR / HR (dist)	distribution parameters
Survey-weighted	`svyglm`	`survey::svyglm()`	yes (design-based)	OR / IRR	-
Additive, proportions, selection	`gam`	`mgcv::gam()`, `mgcv::bam()`	yes	OR / IRR (link)	-
	`betareg`	`betareg::betareg()`	yes	OR (mean link)	-
	`selection`	`sampleSelection::selection()`	no	-	selection component
rms	`ols`	`rms::ols()`	yes	-	-
	`lrm`	`rms::lrm()`	yes	OR	-
	`Glm`	`rms::Glm()`	yes	link-dependent	-
Bayesian	`stanreg`	`rstanarm::stan_glm()`, `rstanarm::stan_glmer()`	yes (draws)	link-dependent	Random effects (if multilevel)
	`brmsfit`	`brms::brm()`	yes (draws)	link-dependent	Random effects (if multilevel)

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):

fit <- loess(dist ~ speed, data = cars)
table_regression(fit)
#> Error in `validate_models_input()`:
#> ! Some `models` are not supported by `table_regression()`.
#> Position 1: `loess` – no `as_regression_frame()` method registered. If support would be useful, please open an issue: https://github.com/amaltawfik/spicy/issues
#> ℹ Run `methods('as_regression_frame')` to see all currently supported model classes.

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:

subset(table_regression_models(), family == "Survival")
#>      family       class                  engine                    ame                 exponentiate
#> 23 Survival       coxph       survival::coxph()       RMST / risk diff                           HR
#> 24 Survival     survreg     survival::survreg() yes + RMST / risk diff TR (log-scale distributions)
#> 25 Survival         cph              rms::cph()                     no                           HR
#> 26 Survival flexsurvreg flexsurv::flexsurvreg()                     no               TR / HR (dist)
#>                     blocks
#> 23                       -
#> 24                       -
#> 25                       -
#> 26 distribution parameters

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).