stargazer: Well-Formatted Regression and Summary Statistics Tables (R package)

'stargazer' is a package for R that produces LaTeX code and ASCII text for well-formatted regression tables (that allow for several models side-by-side), as well as for summary statistics tables. It can also output data frame content directly into LaTeX. You can install this software by typing the following command into the R prompt: > install.packages("stargazer") . You will then be able to load the package in your R programs using library(stargazer) . 'stargazer' is available from CRAN [here], along with its documentation [pdf].

Please send any feedback to hlavac [at] .

A non-technical overview of the package, along with great examples, is here: [pdf]

Citing 'stargazer': If you are using this package in any research that will be published or otherwise distributed to the public, please include the following citation:

Marek Hlavac (2015). stargazer: Well-Formatted Regression and Summary Statistics Tables. 
R package version 5.2.

A BibTeX entry for LaTeX users is:

    title = {stargazer: Well-Formatted Regression and Summary Statistics Tables},
    author = {Marek Hlavac},
    year = {2015},
    note = {R package version 5.2},
    url = {},

Note: If you are getting an error message from your R session when installing, loading or using 'stargazer', please make sure you are running a recent version of R. You can download/upgrade R from [this website].


stargazer supports objects from the most widely used statistical functions and packages. In particular, the package supports model objects from aftreg (eha), arima (stats), betareg (betareg), binaryChoice (sampleSelection), bj (rms), brglm (brglm), censReg (censReg), coeftest (lmtest), coxph (survival), coxreg (eha), clm (ordinal), clogit (survival), cph (rms), dynlm (dynlm), ergm (ergm), errorsarlm (spdev), felm (lfe), gam (mgcv), garchFit (fGarch), gee (gee), glm (stats), Glm (rms), glmer (lme4), glmrob (robustbase), gls (nlme), Gls (rms), gmm (gmm), heckit (sampleSelection), hetglm (glmx), hurdle (pscl), ivreg (AER), lagarlm (spdep), lm (stats), lme (nlme), lmer (lme4), lmrob (robustbase), lrm (rms), maBina (erer), mclogit (mclogit), mlogit (mlogit), mnlogit (mnlogit), mlreg (eha), multinom (nnet), nlme (nlme), nlmer (lme4), ols (rms), pgmm (plm), phreg (eha), plm (plm), pmg (plm), polr (MASS), psm (rms), rem.dyad (relevent), rlm (MASS), rq (quantreg), Rq (rms), selection (sampleSelection), svyglm (survey), survreg (survival), tobit (AER), weibreg (eha), zeroinfl (pscl), as well as from the implementation of these in zelig. In addition, stargazer also supports the following zelig models: “relogit”, “”, “”, “” and “”.

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