Grouped Statistical Analyses in a Tidy Way
person(given = "Indrajeet",
family = "Patil",
role = c("aut", "cre"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531"))
Collection of functions to assist 'broom' and
'broom.mixed' package-related data analysis workflows. In particular,
the generic functions tidy(), glance(), and augment() choose
appropriate S3 methods from these two packages depending on which
package exports the needed method. Additionally, 'grouped_' variants
of the generics provides a convenient way to execute functions across
a combination of grouping variable(s) in a dataframe.
covr, gapminder, generics, ggplot2, knitr, lme4, rmarkdown,
spelling, stringr, testthat, tibble, tidyr
Indrajeet Patil [aut, cre] (<https://orcid.org/0000-0003-1995-6531>)

covr, gapminder, generics, ggplot2, knitr, lme4, rmarkdown,
spelling, stringr, testthat, tibble, tidyr, utils

person(given = "Indrajeet",
family = "Patil",
role = c("aut", "cre", "ctb"),
email = "patilindrajeet.science@gmail.com",
comment = c(ORCID = "0000-0003-1995-6531"))
Collection of functions to assist 'broom' and
'broom.mixed' package-related data analysis workflows. In particular,
the generic functions tidy(), glance(), and augment() choose
appropriate S3 methods from these two packages depending on which
package exports the needed method. Additionally, 'grouped_' and
'boot_' variants of the generics provides a convenient way to execute
functions across a combination of grouping variable(s) in a dataframe
or bootstrap them.
generics, ggplot2, knitr, lme4, rmarkdown, spelling, stringr,
testthat
Indrajeet Patil [aut, cre, ctb]
(<https://orcid.org/0000-0003-1995-6531>)

generics, ggplot2, knitr, lme4, rmarkdown, spelling, stringr,
testthat, tidyr

generics, ggplot2, knitr, lme4, rmarkdown, spelling,
testthat, tidyr

Collection of functions to assist 'broom' and
'broom.mixed' package-related data analysis workflows. In particular,
the generic functions tidy(), glance(), and augment() choose
appropriate S3 methods from these two packages depending on which
package exports the needed method. The 'grouped_' variants of the
generics provides a convenient way to execute functions across a
combination of grouping variable(s) in a dataframe. Additionally,
tidy-and glance-like methods from 'parameters' and 'performance' can
also be accessed.
broom, broom.mixed, dplyr, magrittr, parameters, performance,
rlang, tibble
generics, ggplot2, knitr, lavaan, lme4, MASS, mixor,
rmarkdown, spelling, testthat, tidyr