preparing for CRAN release

ncss-tech · Apr 17, 2024 · 82e215c · 82e215c
1 parent 9786bf5
commit 82e215c
Show file tree

Hide file tree

Showing 5 changed files with 5 additions and 5 deletions.
diff --git a/CITATION.cff b/CITATION.cff
@@ -19,7 +19,7 @@ abstract: The Algorithms for Quantitative Pedology (AQP) project was started in
   the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>.
   These functions and data structures have been extensively tested and documented,
   applied to projects involving hundreds of thousands of soil profiles, and deeply
-  integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>.
+  integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>.
   Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve
   an important role in routine data analysis within the USDA-NRCS Soil Science Division.
   The AQP suite of R packages offer a convenient platform for bridging the gap between

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -7,7 +7,7 @@ Maintainer: Dylan Beaudette <[email protected]>
 Depends: R (>= 3.5.0)
 Imports: grDevices, graphics, stats, utils, methods, grid, lattice, cluster, sp, stringr, data.table, farver
 Suggests: mvtnorm, colorspace, ape, soilDB, sf, latticeExtra, tactile, compositions, sharpshootR, markovchain, xtable, testthat, Gmedian, Hmisc, tibble, RColorBrewer, scales, digest, MASS, mpspline2, soiltexture, gower, knitr, rmarkdown, plyr
-Description: The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
+Description: The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
 License: GPL (>= 3)
 LazyLoad: yes
 Repository: CRAN

diff --git a/README.Rmd b/README.Rmd
@@ -28,7 +28,7 @@ knitr::opts_chunk$set(
 <a href="https://raw.githubusercontent.com/ncss-tech/aqp/master/misc/hexstickers/aqp_sticker_v2.png">
 <img src = "https://raw.githubusercontent.com/ncss-tech/aqp/master/misc/hexstickers/aqp_sticker_v2.png" alt = "aqp hexsticker (Paxton, Montauk, Woodbridge, Ridgebury, Whitman, Catden soil series dendogram)" title = "aqp hexsticker (Paxton, Montauk, Woodbridge, Ridgebury, Whitman, Catden soil series dendogram)" width = "45%" height = "45%" hspace="15" vspace="15" align="right"/></a>
 
-The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
+The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
 
 ## Installation
 

diff --git a/README.md b/README.md
@@ -28,7 +28,7 @@ processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and
 data structures have been extensively tested and documented, applied to
 projects involving hundreds of thousands of soil profiles, and deeply
 integrated into widely used tools such as SoilWeb
-<https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of
+<https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of
 the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve
 an important role in routine data analysis within the USDA-NRCS Soil
 Science Division. The AQP suite of R packages offer a convenient

diff --git a/vignettes/Munsell-color-conversion.Rmd b/vignettes/Munsell-color-conversion.Rmd
@@ -61,7 +61,7 @@ axis(side = 2, las = 1)
 
 Neutral colors are commonly specified two ways in the Munsell system: `N 3/` or `N 3/0`, either format will work with `munsell2rgb()` and `parseMunsell()`.
 
-Non-standard Munsell notation (e.g. `3.6YR 4.4 / 5.6`), possibly collected with a sensor vs. color book, can be approximated with `getClosestMunsellChip()`. A more accurate conversion can be performed with the [`munsellinterpol` package.](https://cran.r-project.org/web/packages/munsellinterpol/index.html).
+Non-standard Munsell notation (e.g. `3.6YR 4.4 / 5.6`), possibly collected with a sensor vs. color book, can be approximated with `getClosestMunsellChip()`. A more accurate conversion can be performed with the [`munsellinterpol` package.](https://cran.r-project.org/package=munsellinterpol).
 
 
 ## Examples