diff --git a/CITATION.cff b/CITATION.cff index 197e6470..e83b2b48 100644 --- 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 . 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 . + integrated into widely used tools such as SoilWeb . 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 index 6e544135..f1c577d2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -7,7 +7,7 @@ Maintainer: Dylan Beaudette 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 . 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 . 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 . 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 . 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 index 7ead3308..8c085df2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -28,7 +28,7 @@ knitr::opts_chunk$set( aqp hexsticker (Paxton, Montauk, Woodbridge, Ridgebury, Whitman, Catden soil series dendogram) -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 . 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 . 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 . 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 . 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 index ecdcc90b..fb959840 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ processing tasks . 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 -. Components of +. 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 index 407d4b5a..5d608c34 100644 --- 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