This is an R data manager meant to avoid manual storage/retrieval of R data to/from the file system. It builds one (or more) centralized repository where R objects are stored with annotations, tags, dependency notes, provenance traces. It also provides navigation tools to easily locate, load and edit previously stored resources.

A data manager meant to avoid manual storage/retrieval of
data to/from the file system. It builds one (or more) centralized
repository where R objects are stored with annotations, tags,
dependency notes, provenance traces. It also provides navigation
tools to easily locate, load and edit previously stored resources.

A data manager meant to avoid manual storage/retrieval of
data to/from the file system. It builds one (or more) centralized
repository where R objects are stored with rich annotations,
including corresponding code chunks, and easily searched and
retrieved.

A data manager meant to avoid manual storage/retrieval of
data to/from the file system. It builds one (or more) centralized
repository where R objects are stored with rich annotations,
including corresponding code chunks, and easily searched and
retrieved. See Napolitano (2017) <doi:10.1037/a0028240> for further
information.

R Interface to the 'NIH RePORTER Project' API
person(given = "Michael",
family = "Barr, ACAS, MAAA, CPCU",
role = c("cre", "aut"),
email = "mike@bikeactuary.com")
Methods to easily build requests in the non-standard JSON
schema required by the National Institute of Health (NIH)'s 'RePORTER
Project API' <https://api.reporter.nih.gov/#/Search/post_v2_projects_search>.
Also retrieve and process result sets as either a ragged or flattened 'tibble'.
devtools, ggplot2, ggrepel, knitr, tinytex, rmarkdown,
scales, tufte, tools, spelling
Michael Barr, ACAS, MAAA, CPCU [cre, aut]
"Michael Barr, ACAS, MAAA, CPCU" <mike@bikeactuary.com>

devtools, ggplot2, ggrepel, knitr, tinytex, rmarkdown,
scales, tufte, spelling

Repeated measures proportional odds logistic regression
Package fits regression model to repeated ordinal response
variables using generalized estimating equations.