🔀 Merge branch:dev_pw into branch:main

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: ndi
 Title: Neighborhood Deprivation Indices
-Version: 0.2.0
-Date: 2025-08-29
+Version: 0.2.1
+Date: 2025-09-04
 Authors@R:
     c(person(given = "Ian D.",
              family = "Buller",

NEWS.md

@@ -1,5 +1,13 @@
 # ndi (development version)
 
+## ndi v0.2.1
+
+### New Features
+* None
+
+### Updates
+* Fixed broken URLs in 'theil.Rd', 'ndi-package.Rd', 'ndi2.html', README, and NEWS
+
 ## ndi v0.2.0
 
 ### New Features
@@ -9,7 +17,7 @@
 * Added `hoover()` function to compute the aspatial racial or ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan, Cuzzort, & Duncan (1961; LC:60007089)
 * Added `james_taeuber()` function to compute the aspatial racial or ethnic Dissimilarity Index (*D*) based on [James & Taeuber (1985)](https://doi.org/10.2307/270845)
 * Added `lieberson()` function to compute the aspatial racial or ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and and [Bell (1954)](https://doi.org/10.2307/2574118)
-* Added `theil()` function the aspatial racial or ethnic Entropy (*H*) based on Theil (1972; ISBN:978-0-444-10378-9) and [Theil & Finizza (1971)](https://doi.org/110.1080/0022250X.1971.9989795)
+* Added `theil()` function the aspatial racial or ethnic Entropy (*H*) based on Theil (1972; ISBN:978-0-444-10378-9) and [Theil & Finizza (1971)](https://doi.org/10.1080/0022250X.1971.9989795)
 * Added `white_blau()` function to compute an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0)
 * Thank you for the feature suggestions above, [Symielle Gaston](https://orcid.org/0000-0001-9495-1592)
 * Added `denton()` function to compute the aspatial racial or ethnic Relative Clustering (*RCL*) based on [Massey & Denton (1988)](https://doi.org/10.1093/sf/67.2.281)

R/ndi-package.R

@@ -26,7 +26,7 @@
 #' 
 #' \code{\link{sudano}} Computes the aspatial Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}.
 #' 
-#' \code{\link{theil}} Computes the aspatial Entropy (\emph{H}) based on Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) \doi{110.1080/0022250X.1971.9989795}.
+#' \code{\link{theil}} Computes the aspatial Entropy (\emph{H}) based on Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) \doi{10.1080/0022250X.1971.9989795}.
 #' 
 #' \emph{Indices of Racial or Ethnic Residential Exposure}
 #' 
@@ -64,7 +64,7 @@
 #'
 #' \code{\link{morgan_massey}} Computes the aspatial Distance-Decay Isolation Index (\emph{DPxx\*}) based on Morgan (1986) \url{https://www.jstor.org/stable/20001935} and Massey & Denton (1988) \doi{10.1093/sf/67.2.281}.
 #' 
-#' \code{\link{white_blau}} Computes an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} \url{} and Blau (1977; ISBN-13:978-0-029-03660-0).
+#' \code{\link{white_blau}} Computes an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0).
 #' 
 #' \strong{Additional Indices of Socioeconomic Disparity}
 #' 

R/theil.R

@@ -10,7 +10,7 @@
 #' @param quiet Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.
 #' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics
 #'
-#' @details This function will compute the aspatial Entropy (\emph{H}) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) \doi{110.1080/0022250X.1971.9989795}. This function provides the computation of \emph{H} for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).
+#' @details This function will compute the aspatial Entropy (\emph{H}) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) \doi{10.1080/0022250X.1971.9989795}. This function provides the computation of \emph{H} for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).
 #'
 #' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for the aspatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available (2010 onward for \code{geo_large = 'cbsa'} and 2011 onward for \code{geo_large = 'place'}, \code{geo_large = 'csa'}, or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial or ethnic subgroups (U.S. Census Bureau definitions) are:
 #' \itemize{

README.md

@@ -12,11 +12,11 @@
 [![](https://img.shields.io/badge/DOI-10.32614/CRAN.package.ndi-1f57b6?style=flat&link=https://doi.org/10.32614/CRAN.package.ndi)](https://doi.org/10.32614/CRAN.package.ndi)
 <!-- badges: end -->
 
-**Date repository last updated**: 2025-08-29
+**Date repository last updated**: 2025-09-04
 
 ### Overview
 
-Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available: including: (1) based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x) and (2) based on [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) who use variables chosen by [Roux and Mair (2010)](https://doi.org/:10.1111/j.1749-6632.2009.05333.x). Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community  Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in [Massey & Denton (1988)](https://doi.org/10.1093/sf/67.2.281), and selected metrics of socioeconomic deprivation and disparity.
+Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available: including: (1) based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x) and (2) based on [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) who use variables chosen by [Roux and Mair (2010)](https://doi.org/10.1111/j.1749-6632.2009.05333.x). Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community  Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in [Massey & Denton (1988)](https://doi.org/10.1093/sf/67.2.281), and selected metrics of socioeconomic deprivation and disparity.
 
 ### Installation
 
@@ -132,7 +132,7 @@ To install the development version from GitHub:
 </tr>
 <tr>
 <td><a href='/R/theil.R'><code>theil</code></a></td>
-<td>Compute the aspatial racial or ethnic Entropy (<i>H</i>) based on Theil (1972; ISBN-13:978-0-444-10378-9) and <a href='https://doi.org/110.1080/0022250X.1971.9989795'>Theil & Finizza (1971)</a></td>
+<td>Compute the aspatial racial or ethnic Entropy (<i>H</i>) based on Theil (1972; ISBN-13:978-0-444-10378-9) and <a href='https://doi.org/10.1080/0022250X.1971.9989795'>Theil & Finizza (1971)</a></td>
 </tr>
 <tr>
 <td><a href='/R/white.R'><code>white</code></a></td>
@@ -1782,7 +1782,7 @@ This package was originally developed while the author was a postdoctoral fellow
 
 ### Acknowledgments
 
-The [`messer()`](R/messer.R) function functionalizes the code found in [Hruska et al. (2022)](https://doi.org/10.1016/j.janxdis.2022.102529) available on an [OSF repository](https://doi.org/10.17605/OSF.IO/M2SAV), but with percent with income less than $30K added to the computation based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x). The [`messer()`](R/messer.R) function also allows for the computation of *NDI* (Messer) for each year between 2010-2020 (when the U.S. census characteristics are available to date). There was no code companion to compute *NDI* (Powell-Wiley) included in [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) or [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) only a [description](https://www.gis.cancer.gov/research/NeighDeprvIndex_Methods.pdf), but the package author worked directly with the latter manuscript authors to replicate their [*SAS*](https://www.sas.com) code in [**R**](https://cran.r-project.org/) for the [`powell_wiley()`](R/powell_wiley.R) function. See the Accumulating Data to Optimally Predict Obesity Treatment [(ADOPT)](https://gis.cancer.gov/research/adopt.html) Core Measures Project for more details. Please note: the *NDI* (Powell-Wiley) values will not exactly match (but will highly correlate with) those found in [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) because the two studies used a different statistical platform (i.e., [*SPSS*](https://www.ibm.com/spss) and [*SAS*](https://www.sas.com), respectively) that intrinsically calculate the principal component analysis differently from [**R**](https://cran.r-project.org/). The internal function to calculate the Atkinson Index with the Hölder mean is based on the `Atkinson()` function in the [*DescTools*](https://cran.r-project.org/package=DescTools) package.
+The [`messer()`](R/messer.R) function functionalizes the code found in [Hruska et al. (2022)](https://doi.org/10.1016/j.janxdis.2022.102529) available on an [OSF repository](https://doi.org/10.17605/OSF.IO/M2SAV), but with percent with income less than $30K added to the computation based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x). The [`messer()`](R/messer.R) function also allows for the computation of *NDI* (Messer) for each year between 2010-2020 (when the U.S. census characteristics are available to date). There was no code companion to compute *NDI* (Powell-Wiley) included in [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) or [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) only a [description](https://www.gis.cancer.gov/research/NeighDeprvIndex_Methods.pdf), but the package author worked directly with the latter manuscript authors to replicate their [*SAS*](https://www.sas.com) code in [**R**](https://cran.r-project.org/) for the [`powell_wiley()`](R/powell_wiley.R) function. See the Accumulating Data to Optimally Predict Obesity Treatment [(ADOPT)](https://gis.cancer.gov/research/adopt.html) Core Measures Project for more details. Please note: the *NDI* (Powell-Wiley) values will not exactly match (but will highly correlate with) those found in [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) because the two studies used a different statistical platform (i.e., [*SPSS*](https://www.ibm.com/products/spss) and [*SAS*](https://www.sas.com), respectively) that intrinsically calculate the principal component analysis differently from [**R**](https://cran.r-project.org/). The internal function to calculate the Atkinson Index with the Hölder mean is based on the `Atkinson()` function in the [*DescTools*](https://cran.r-project.org/package=DescTools) package.
 
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