Substantive significance testing for regression estimates and
marginal effects.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).

Domain mean estimation with monotonicity or block monotone constraints.
See Wu J, Meyer MC and Opsomer JD (2016)<doi:10.1002/cjs.11301> for more details.

Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.
coneproj, purrr, stats, graphics, grDevices, MASS, Matrix

person("Xiyue", "Liao", , "xiyue.liao@csulb.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-4508-9219"))
Domain mean estimation with monotonicity or block monotone
constraints. See Xu X, Meyer MC and Opsomer JD
(2021)<doi:10.1016/j.jspi.2021.02.004> for more details.
coneproj, graphics, grDevices, Matrix, tidyselect, dplyr,
stats

Common Base-R Problems Relating to Lists
c(
person(c("Richard","Aubrey"), "White", email = "hello@rwhite.no", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6747-1726")),
person(given = "CSIDS", role = c("cph"))
)
Utility functions that help with common base-R problems relating to lists.
Lists in base-R are very flexible. This package provides functions to quickly and easily
characterize types of lists. That is, to identify if all elements in a list
are null, data.frames, lists, or fully named lists. Other functionality is provided
for the handling of lists, such as the easy splitting of lists into equally sized
groups, and the unnesting of data.frames within fully named lists.
testthat, knitr, rmarkdown, rstudioapi, glue
Richard Aubrey White [aut, cre]
(<https://orcid.org/0000-0002-6747-1726>),
CSIDS [cph]

Read and Write CSV Files with Selected Conventions
Reads and writes CSV with selected conventions.
Uses the same generic function for reading and writing to promote consistent formats.

This package provides functions for loading large (10M+ lines) CSV
and other delimited files, similar to read.csv, but typically faster and
using less memory than the standard R loader. While not entirely general,
it covers many common use cases when the types of columns in the CSV file
are known in advance. In addition, the package provides a class 'int64',
which represents 64-bit integers exactly when reading from a file. The
latter is useful when working with 64-bit integer identifiers exported from
databases. The CSV file loader supports common column types including
'integer', 'double', 'string', and 'int64', leaving further type
transformations  to the user.