Description: Provides functions related to human natural ordering.
It handles adjacent digits in a character sequence as a number so that
natural sort function arranges a character vector by their numbers, not digit
characters. It is typically seen when operating systems lists file names. For
example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1
< 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms
arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth

it into a-1.png, a-10.png, a-2.png owing to their third characters.

Title: Regression with NA Values in Unordered Factors
email = "eagerstats@gmail.com", role = c("aut","cre"))
Description: Fits regressions where unordered factors can be set to NA in
subsets of the data where they are not applicable or otherwise not
contrastive by using sum contrasts and setting NA values to zero.
Imports: methods, utils, MASS (>= 7.3-45), stringr (>= 1.1.0), Matrix
Maintainer: Christopher D. Eager <eagerstats@gmail.com>

Suggests: testthat, knitr, rmarkdown, afex

Title: Navigation Menu for Pipe-Friendly Data Processing
Description: Provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures.
By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.
Imports: cli, dplyr, pillar, purrr, rlang, tibble, tidyselect, vctrs

Imports: cli, dplyr, pillar, purrr, rlang, tidyselect, vctrs

Imports: cli, lifecycle, pillar, purrr, rlang, stickyr, tidyselect,
Suggests: covr, dplyr, testthat (>= 3.0.0)

Title: Navigated Weighting for the Inverse Probability Weighting
Authors@R: person("Hiroto", "Katsumata", email = "hrt.katsumata@gmail.com", role = c("aut", "cre"))
Description: Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
<arXiv:2005.10998>, which improves the inverse probability weighting by
utilizing estimating equations suitable for a specific pre-specified parameter
of interest (e.g., the average treatment effects or the average treatment
effects on the treated) in propensity score estimation. It includes the
covariate balancing propensity score proposed by Imai and Ratkovic (2014)
<doi:10.1111/rssb.12027>, which uses covariate balancing conditions in
propensity score estimation. The point estimate of the parameter of interest
as well as coefficients for propensity score estimation and their
uncertainty are produced using the M-estimation. The same functions can be
used to estimate average outcomes in missing outcome cases.
Packaged: 2020-06-08 11:53:51 UTC; hkatsumata