Supported Models

marginaleffects effects supports 97 model types directly, and dozens more via the tidymodels and mlr3 frameworks. This table shows the list of directly supported model types. There are three main alternative software packages to compute such slopes (1) Stata’s margins command, (2) R’s margins::margins() function, and (3) R’s emmeans::emtrends() function. The test suite hosted on Github compares the numerical equivalence of results produced by marginaleffects::slopes() to those produced by all 3 alternative software packages:

  • ✓: a green check means that the results of at least one model are equal to a reasonable tolerance.
  • ✖: a red cross means that the results are not identical; extra caution is warranted.
  • U: a grey U means that computing slopes for a model type is unsupported by alternative packages, but supported by marginaleffects.
  • An empty cell means means that no comparison has been made yet.

I am eager to add support for new models. Feel free to file a request or submit code on Github.

Numerical equivalence
Supported by marginaleffects
Stata
margins
emtrends
Package Function dY/dX SE dY/dX SE dY/dX SE
stats lm ✓ ✓ ✓ ✓ ✓ ✓
glm ✓ ✓ ✓ ✓ ✓ ✓
nls
loess ✓ U U
AER ivreg ✓ ✓ ✓ ✓ U U
tobit ✓ ✓ U U ✓ ✓
afex afex_aov U U ✓ ✓
aod betabin U U U U
betareg betareg ✓ ✓ ✓ ✓ ✓ ✓
bife bife U U U U
biglm biglm U U U U
bigglm U U U U
blme blmer
bglmer
brglm2 bracl U U U U
brglmFit ✓ ✓ ✓ ✓
brnb ✓ ✓ U U
brmultinom U U U U
brms brm U U ✓ ✓
crch crch U U U U
hxlr U U U U
DCchoice oohbchoice
estimatr lm_lin
lm_robust ✓ ✓ ✓ U ✓ ✓
iv_robust ✓ ✓ U U U U
flexsurv flexsurvreg
flexsurvspline
fixest feols ✓ ✓ U U U U
feglm U U U U
fenegbin U U U U
fepois ✓ ✓ U U U U
gam gam U U ✓ ✓
gamlss gamlss U U ✓ ✓
geepack geeglm U U ✓ ✓
glmmTMB glmmTMB U U ✓ ✓
glmx glmx ✓ U U U
ivreg ivreg ✓ ✓ ✓ ✓ U U
mlr3 Learner
lme4 lmer ✓ ✓ ✓ ✓ ✓ ✓
glmer ✓ ✓ ✓ ✓ ✓ ✓
glmer.nb ✓ ✓ ✓ ✓
lmerTest lmer ✓ ✓ ✓ ✓
logistf logistf
flic
flac
MASS glmmPQL U U ✓ ✓
glm.nb ✓ ✓ ✓ ✓ ✓ ✓
polr ✓ ✓ ✖ ✖ ✓ ✓
rlm ✓ ✓ ✓ ✓
mclogit mblogit U U U U
mclogit U U U U
MCMCglmm MCMCglmm U U U U U U
mgcv gam U U ✓ ✓
bam U U ✓ ✖
mhurdle mhurdle ✓ ✓ U U
mlogit mlogit U U U U
mvgam mvgam
nlme gls U U ✓ ✓
lme
nnet multinom ✓ ✓ U U U U
ordbetareg ordbetareg U U
ordinal clm ✓ ✓ U U U U
plm plm ✓ ✓ ✓ ✓ U U
phylolm phylolm
phyloglm
pscl hurdle ✓ U ✓ ✖
hurdle ✓ U ✓ ✖
zeroinfl ✓ ✓ ✓ U ✓ ✓
quantreg rq ✓ ✓ U U ✓ ✓
Rchoice hetprob
ivpml
REndo latentIV
copulaCorrection
higherMomentsIV
hetErrorsIV
multilevelIV
rms ols
lrm
orm
Gls
robust lmRob U U U U
robustbase glmrob ✓ ✓ U U
lmrob ✓ ✓ U U
robustlmm rlmer U U
rstanarm stan_glm ✖ U ✓ ✓
sampleSelection selection U U U U
heckit U U U U
scam scam U U U U
speedglm speedglm ✓ ✓ ✓ ✓ U U
speedlm ✓ ✓ ✓ ✓ U U
survey svyglm ✓ ✓ ✓ ✓
svyolr
survival clogit
coxph ✓ ✓ U U ✓ ✓
survreg
tobit1 tobit1 ✓ ✓ U U
truncreg truncreg ✓ ✓ ✓ ✓ U U