The package 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 characters.

Regression with NA Values in Unordered Factors
person("Christopher D.", "Eager",
email = "eagerstats@gmail.com", role = c("aut","cre"))
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.

Navigation Menu for Pipe-Friendly Data Processing
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.
cli, dplyr, pillar, purrr, rlang, tibble, tidyselect, vctrs

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

cli, lifecycle, pillar, purrr, rlang, stickyr, tidyselect,
vctrs

Navigated Weighting for the Inverse Probability Weighting
person("Hiroto", "Katsumata", email = "hrt.katsumata@gmail.com", role = c("aut", "cre"))
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.