polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Suggests: knitr, rmarkdown, schlegel, ggplot2, devtools

the two cases with confidence interval for lm() [linear model], glm() [generalised linear model], glm.nb() [negative binomial model],
polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model],
svyglm() [survey-weighted generalised linear models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Depends: R (>= 3.5.0), stats, MASS, parallel, mlogit, dfidx, survey
Suggests: knitr, rmarkdown, ggplot2, devtools

Title: Fitting Generalized Linear Models
Author: Ian Marschner (using code from glm and glm.fit in the stats
Description: Fits generalized linear models using the same model
specification as glm in the stats package, but with a modified
default fitting method that provides greater stability for
models that may fail to converge using glm

Author: Ian Marschner (using code from glm and glm.fit in the stats package)
Description: Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm

Authors@R: c(person("Ian", "Marschner", role = c("aut", "cre"),
comment = "using code from glm and glm.fit in the stats package"))
Description: Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Author: Ian Marschner [aut, cre] (using code from glm and glm.fit in the stats

Authors@R: c(person("Ian", "Marschner", role = "aut",
comment = "using code from glm and glm.fit in the stats package"),
person("Mark W.", "Donoghoe", role = c("cre", "ctb"),
Author: Ian Marschner [aut] (using code from glm and glm.fit in the stats