Model formula like y ~ x1 + x2 (for lm, glm) or y ~ x1 + x2 + (1 | group) (for lmer, glmer).

Data frame.

[optional] The same as in glm and glmer (e.g., family=binomial will fit a logistic model).

Number of decimal places of output. Default is 3.

[only for lm and glm] FALSE (default), TRUE (then the default is "HC1"), "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", or "HC5". It will add a table with heteroskedasticity-robust standard errors (aka. Huber-White standard errors). For details, see ?sandwich::vcovHC and ?jtools::summ.lm.

*** "HC1" is the default of Stata, whereas "HC3" is the default suggested by the sandwich package.

[only for lm and glm] Cluster-robust standard errors are computed if cluster is set to the name of the input data's cluster variable or is a vector of clusters. If you specify cluster, you may also specify the type of robust. If you do not specify robust, "HC1" will be set as default.

[only for lmer] [optional] Default is NULL. If you have predictors at level 2, besides putting them into the formula in the lmer function as usual, you may also define here the level-2 grouping/clustering variables and corresponding level-2 predictor variables.

*** Example: level2.predictors="School: W1 + W2; House: 1", where School and House are two grouping variables, W1 & W2 are school-level predictors, and there is no house-level predictor.

*** If there is no level-2 predictor in the formula of lmer, just leave this parameter blank.

[only for lmer] Manually setting variable types. Needless in most situations.

[only for lmer] TRUE or FALSE (default). Test random effects (i.e., variance components) by using the likelihood-ratio test (LRT), which is asymptotically chi-square distributed. For large datasets, it is much time-consuming.