From 0024506490acf67e3957698fe709eefbd7811490 Mon Sep 17 00:00:00 2001 From: Laura A DeCicco Date: Tue, 5 Nov 2024 07:59:46 -0600 Subject: [PATCH 1/2] Cleaning up links --- R/readNWISSample.R | 5 +- README.Rmd | 8 +-- README.md | 149 ++++++++++++++++++++++++----------------- man/readNWISSample.Rd | 5 +- vignettes/EGRET.Rmd | 2 +- vignettes/Overview.Rmd | 8 +-- 6 files changed, 99 insertions(+), 78 deletions(-) diff --git a/R/readNWISSample.R b/R/readNWISSample.R index 331e8e16..7aedc0b9 100644 --- a/R/readNWISSample.R +++ b/R/readNWISSample.R @@ -1,8 +1,7 @@ #' Import NWIS Sample Data for EGRET analysis #' #' Imports data from NWIS web service. -#' A list of parameter and statistic codes can be found here: \url{https://help.waterdata.usgs.gov/codes-and-parameters} -#' For raw data, use \code{\link[dataRetrieval]{readNWISqw}} from the dataRetrieval package. +#' For raw data, use \code{\link[dataRetrieval]{readWQPqw}} from the dataRetrieval package. #' This function will retrieve the raw data, and compress it (summing constituents) if #' more than 1 parameter code is supplied. See #' section 3.2.4 of the vignette for more details. @@ -33,7 +32,7 @@ #' CosDY \tab numeric \tab Cosine of the DecYear #' } #' @seealso \code{\link{compressData}}, \code{\link{populateSampleColumns}}, -#' \code{\link[dataRetrieval]{readNWISqw}} +#' \code{\link[dataRetrieval]{readWQPqw}} #' @examples #' \donttest{ #' # These examples require an internet connection to run diff --git a/README.Rmd b/README.Rmd index f2c7fdaf..c28dd93f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -26,13 +26,13 @@ Exploration and Graphics for RivEr Trends (`EGRET`): An R-package for the analysis of long-term changes in water quality and streamflow, including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS). Look for new and improved documentation here: - + The link for the official USGS publication user guide is here: [https://pubs.usgs.gov/tm/04/a10/](https://pubs.usgs.gov/tm/04/a10/) -A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci/) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. +A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. If you are familiar with the traditional `EGRET` workflow, check out the [Overview and Updates](https://doi-usgs.github.io/EGRET/articles/Overview.html to see how all the latest updates relate. @@ -218,7 +218,7 @@ sessioninfo::session_info() ## Reporting bugs Please consider reporting bugs and asking questions on the Issues page: -[https://github.com/DOI-USGS/EGRET/issues](https://github.com/DOI-USGS/EGRET/issues) + ## Subscribe @@ -251,7 +251,7 @@ citation(package = "EGRET") See this list for WRTDS applications in print: - + ```{r disclaimer, child="DISCLAIMER.md", eval=TRUE} diff --git a/README.md b/README.md index eb82235a..f7f0175b 100644 --- a/README.md +++ b/README.md @@ -11,14 +11,13 @@ including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS). Look for new and improved documentation here: -https://doi-usgs.github.io/EGRET/\ + The link for the official USGS publication user guide is here: -A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci/) +A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. @@ -328,13 +327,37 @@ siteID <- "01491000" #Choptank River at Greensboro, MD startDate <- "" # Get earliest date endDate <- "" # Get latest date Daily <- readNWISDaily(siteID, "00060", startDate, endDate) -#> GET: https://waterservices.usgs.gov/nwis/dv/?site=01491000&format=rdb,1.0&ParameterCd=00060&StatCd=00003&startDT=1851-01-01 -#> There are 28058 data points, and 28058 days. +#> +#> GET +#> https://waterservices.usgs.gov/nwis/dv/?site=01491000&format=rdb%2C1.0&ParameterCd=00060&StatCd=00003&startDT=1851-01-01 +#> Headers: +#> • Accept-Encoding: 'compress' +#> • Accept-Encoding: 'gzip' +#> • Accept-Encoding: 'deflate' +#> Body: empty +#> Options: +#> • useragent: 'libcurl/8.3.0 httr2/1.0.5 dataRetrieval/2.7.17.1' +#> Policies: +#> • throttle_delay: a function +#> • retry_max_tries: 3 +#> • retry_on_failure: FALSE +#> • retry_backoff: a object +#> There are 28068 data points, and 28068 days. # Gather site and parameter information: # Here user must input some values for # the default (interactive=TRUE) INFO <- readNWISInfo(siteID, "00060") -#> GET: https://waterservices.usgs.gov/nwis/site/?siteOutput=Expanded&format=rdb&site=01491000 +#> +#> GET +#> https://waterservices.usgs.gov/nwis/site/?siteOutput=Expanded&format=rdb&site=01491000 +#> Body: empty +#> Options: +#> • useragent: 'libcurl/8.3.0 httr2/1.0.5 dataRetrieval/2.7.17.1' +#> Policies: +#> • throttle_delay: a function +#> • retry_max_tries: 3 +#> • retry_on_failure: FALSE +#> • retry_backoff: a object #> Your site for streamflow data is: #> 01491000 . #> Your site name is CHOPTANK RIVER NEAR GREENSBORO, MD @@ -422,63 +445,65 @@ sessioninfo::session_info() #> collate English_United States.utf8 #> ctype English_United States.utf8 #> tz America/Chicago -#> date 2024-10-26 +#> date 2024-11-05 #> pandoc 3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── -#> package * version date (UTC) lib source -#> bit 4.5.0 2024-09-20 [1] CRAN (R 4.4.1) -#> bit64 4.5.2 2024-09-22 [1] CRAN (R 4.4.1) -#> class 7.3-22 2023-05-03 [2] CRAN (R 4.4.1) -#> classInt 0.4-10 2023-09-05 [1] CRAN (R 4.4.0) -#> cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1) -#> crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1) -#> curl 5.2.3 2024-09-20 [1] CRAN (R 4.4.1) -#> dataRetrieval 2.7.17 2024-10-25 [1] local -#> DBI 1.2.3 2024-06-02 [1] CRAN (R 4.4.0) -#> digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1) -#> dotCall64 1.2 2024-10-04 [1] CRAN (R 4.4.1) -#> e1071 1.7-16 2024-09-16 [1] CRAN (R 4.4.1) -#> EGRET * 3.0.10 2024-10-26 [1] local -#> evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1) -#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0) -#> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0) -#> fields 16.3 2024-09-30 [1] CRAN (R 4.4.1) -#> glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1) -#> highr 0.11 2024-05-26 [1] CRAN (R 4.4.0) -#> hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0) -#> htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0) -#> httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0) -#> KernSmooth 2.23-24 2024-05-17 [2] CRAN (R 4.4.1) -#> knitr 1.48 2024-07-07 [1] CRAN (R 4.4.1) -#> lattice 0.22-6 2024-03-20 [1] CRAN (R 4.4.0) -#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0) -#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0) -#> maps 3.4.2 2023-12-15 [1] CRAN (R 4.4.0) -#> Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1) -#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0) -#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0) -#> proxy 0.4-27 2022-06-09 [1] CRAN (R 4.4.0) -#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0) -#> Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.1) -#> readr 2.1.5 2024-01-10 [1] CRAN (R 4.4.0) -#> rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.1) -#> rmarkdown 2.28 2024-08-17 [1] CRAN (R 4.4.1) -#> rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.4.1) -#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0) -#> sf 1.0-18 2024-10-11 [1] CRAN (R 4.4.1) -#> spam 2.11-0 2024-10-03 [1] CRAN (R 4.4.1) -#> survival 3.6-4 2024-04-24 [2] CRAN (R 4.4.1) -#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.0) -#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0) -#> tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0) -#> units 0.8-5 2023-11-28 [1] CRAN (R 4.4.0) -#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0) -#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0) -#> viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.0) -#> vroom 1.6.5 2023-12-05 [1] CRAN (R 4.4.0) -#> xfun 0.48 2024-10-03 [1] CRAN (R 4.4.1) -#> yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1) +#> package * version date (UTC) lib source +#> bit 4.5.0 2024-09-20 [1] CRAN (R 4.4.1) +#> bit64 4.5.2 2024-09-22 [1] CRAN (R 4.4.1) +#> class 7.3-22 2023-05-03 [2] CRAN (R 4.4.1) +#> classInt 0.4-10 2023-09-05 [1] CRAN (R 4.4.0) +#> cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1) +#> crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1) +#> curl 5.2.3 2024-09-20 [1] CRAN (R 4.4.1) +#> dataRetrieval 2.7.17.1 2024-11-05 [1] local +#> DBI 1.2.3 2024-06-02 [1] CRAN (R 4.4.0) +#> digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1) +#> dotCall64 1.2 2024-10-04 [1] CRAN (R 4.4.1) +#> e1071 1.7-16 2024-09-16 [1] CRAN (R 4.4.1) +#> EGRET * 3.0.10 2024-11-01 [1] local +#> evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1) +#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0) +#> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0) +#> fields 16.3 2024-09-30 [1] CRAN (R 4.4.1) +#> glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1) +#> highr 0.11 2024-05-26 [1] CRAN (R 4.4.0) +#> hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0) +#> htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0) +#> httr2 1.0.5 2024-09-26 [1] CRAN (R 4.4.1) +#> KernSmooth 2.23-24 2024-05-17 [2] CRAN (R 4.4.1) +#> knitr 1.48 2024-07-07 [1] CRAN (R 4.4.1) +#> lattice 0.22-6 2024-03-20 [1] CRAN (R 4.4.0) +#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0) +#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0) +#> maps 3.4.2 2023-12-15 [1] CRAN (R 4.4.0) +#> Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1) +#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0) +#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0) +#> proxy 0.4-27 2022-06-09 [1] CRAN (R 4.4.0) +#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0) +#> rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0) +#> Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.1) +#> readr 2.1.5 2024-01-10 [1] CRAN (R 4.4.0) +#> rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.1) +#> rmarkdown 2.28 2024-08-17 [1] CRAN (R 4.4.1) +#> rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.4.1) +#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0) +#> sf 1.0-18 2024-10-11 [1] CRAN (R 4.4.1) +#> spam 2.11-0 2024-10-03 [1] CRAN (R 4.4.1) +#> survival 3.6-4 2024-04-24 [2] CRAN (R 4.4.1) +#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.0) +#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0) +#> tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0) +#> units 0.8-5 2023-11-28 [1] CRAN (R 4.4.0) +#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0) +#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0) +#> viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.0) +#> vroom 1.6.5 2023-12-05 [1] CRAN (R 4.4.0) +#> withr 3.0.1 2024-07-31 [1] CRAN (R 4.4.1) +#> xfun 0.48 2024-10-03 [1] CRAN (R 4.4.1) +#> yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1) #> #> [1] C:/Users/ldecicco/AppData/Local/R/win-library/4.4 #> [2] C:/Program Files/R/R-4.4.1/library @@ -546,9 +571,7 @@ citation(package = "EGRET") See this list for WRTDS applications in print: -https://doi-usgs.github.io/EGRET/articles/References_WRTDS.html\ + # Disclaimer diff --git a/man/readNWISSample.Rd b/man/readNWISSample.Rd index 7a6dcd52..91845d9b 100644 --- a/man/readNWISSample.Rd +++ b/man/readNWISSample.Rd @@ -40,8 +40,7 @@ CosDY \tab numeric \tab Cosine of the DecYear } \description{ Imports data from NWIS web service. -A list of parameter and statistic codes can be found here: \url{https://help.waterdata.usgs.gov/codes-and-parameters} -For raw data, use \code{\link[dataRetrieval]{readNWISqw}} from the dataRetrieval package. +For raw data, use \code{\link[dataRetrieval]{readWQPqw}} from the dataRetrieval package. This function will retrieve the raw data, and compress it (summing constituents) if more than 1 parameter code is supplied. See section 3.2.4 of the vignette for more details. @@ -55,7 +54,7 @@ Sample_01075 <- readNWISSample('01594440','01075', '1985-01-01', '1985-03-31') } \seealso{ \code{\link{compressData}}, \code{\link{populateSampleColumns}}, -\code{\link[dataRetrieval]{readNWISqw}} +\code{\link[dataRetrieval]{readWQPqw}} } \keyword{USGS} \keyword{WRTDS} diff --git a/vignettes/EGRET.Rmd b/vignettes/EGRET.Rmd index 3537bb9b..48fd3ea5 100644 --- a/vignettes/EGRET.Rmd +++ b/vignettes/EGRET.Rmd @@ -415,7 +415,7 @@ knitr::kable(DF, caption="Example data", ``` -EGRET will "add up" all the values in a given row to form the total for that sample when using the Sample data frame. Thus, you only want to enter data that should be added together. If you want a data frame with multiple constituents that are not summed, do not use `readNWISSample`, `readWQPSample`, or `readUserSample`. The raw data functions: `getWQPdata`, `readNWISqw`, `readWQPqw`, `readWQPdata` from the EGRET package will not sum constituents, but leave them in their individual columns. +EGRET will "add up" all the values in a given row to form the total for that sample when using the Sample data frame. Thus, you only want to enter data that should be added together. If you want a data frame with multiple constituents that are not summed, do not use `readNWISSample`, `readWQPSample`, or `readUserSample`. The raw data functions: `getWQPdata`, `readWQPqw`, `readWQPdata` from the EGRET package will not sum constituents, but leave them in their individual columns. For example, we might know the value for dp on 5/30/2005, but we don't want to put it in the table because under the rules of this data set, we are not supposed to add it in to the values in 2005. diff --git a/vignettes/Overview.Rmd b/vignettes/Overview.Rmd index a97b6d6c..e1c9ae2d 100644 --- a/vignettes/Overview.Rmd +++ b/vignettes/Overview.Rmd @@ -34,7 +34,7 @@ There are analyses in EGRET that work only with discharge data (we call them Flo # 1. EGRET overview and data entry -These topics are covered extensively both in the [Introduction to the EGRET package](https://doi-usgs.github.io/EGRET) and in more detail in the [EGRET User Guide](https://pubs.usgs.gov/tm/04/a10/pdf/tm4A10.pdf). +These topics are covered extensively both in the [Introduction to the EGRET package](https://doi-usgs.github.io/EGRET/) and in more detail in the [EGRET User Guide](https://pubs.usgs.gov/tm/04/a10/pdf/tm4A10.pdf). # 2. Exploration of trends in discharge @@ -44,7 +44,7 @@ In addition, a script is available with some new capabilities. It is called [Da # 3. Describing water quality without using the WRTDS model. -In some situations, the analyst may want to simply display a set of water quality data without any use of models or modeling assumptions. This can be a useful addition to showing results derived from the WRTDS statistical model. These are described in section 6 of [Introduction to the EGRET package](https://doi-usgs.github.io/EGRET) as well as on pages 29 -37 of the [EGRET User Guide](https://pubs.usgs.gov/tm/04/a10/pdf/tm4A10.pdf). +In some situations, the analyst may want to simply display a set of water quality data without any use of models or modeling assumptions. This can be a useful addition to showing results derived from the WRTDS statistical model. These are described in section 6 of [Introduction to the EGRET package](https://doi-usgs.github.io/EGRET/) as well as on pages 29 -37 of the [EGRET User Guide](https://pubs.usgs.gov/tm/04/a10/pdf/tm4A10.pdf). ### Improved graphical treatment of censored data. @@ -58,7 +58,7 @@ Having said that, we should recognize that the purpose of many of these graphs o # 4. Making the best possible estimates of concentration or flux for some specific day, month, or year. -Although the primary purpose for the development of the WRTDS method and EGRET software was to evaluate long term trends, they can also be used for this different purpose: creating the best possible estimate of conditions for any specific day, month, season, or year. For trend analysis (which is discussed in item 5 below) our objective is to remove the confounding influence of the year-to-year variations in discharge in order to see more clearly the underlying trend in water quality. The results from the WRTDS method that are used for the trend analysis purpose are the "Flow-Normalized" values. But here we are considering a different kind of purpose. Here, the question we are focused on is something like: "What’s our best estimate of the flux of total phosphorus into the lake during the month of May 2020?" or "What’s our best estimate of the chloride concentration on February 2, 2019?" For these kinds of estimates we can use a new method called "WRTDS Kalman" or [WRTDS_K](https://doi-usgs.github.io/EGRET/articles/WRTDSK.html). In addition there are two publications that describe the concept and the mathematics. [Lee, Hirsch and Crawford, 2019](https://pubs.er.usgs.gov/publication/sir20195084) and [Zhang and Hirsch, 2019](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019WR025338). +Although the primary purpose for the development of the WRTDS method and EGRET software was to evaluate long term trends, they can also be used for this different purpose: creating the best possible estimate of conditions for any specific day, month, season, or year. For trend analysis (which is discussed in item 5 below) our objective is to remove the confounding influence of the year-to-year variations in discharge in order to see more clearly the underlying trend in water quality. The results from the WRTDS method that are used for the trend analysis purpose are the "Flow-Normalized" values. But here we are considering a different kind of purpose. Here, the question we are focused on is something like: "What’s our best estimate of the flux of total phosphorus into the lake during the month of May 2020?" or "What’s our best estimate of the chloride concentration on February 2, 2019?" For these kinds of estimates we can use a new method called "WRTDS Kalman" or [WRTDS_K](https://doi-usgs.github.io/EGRET/articles/WRTDSK.html). In addition there are two publications that describe the concept and the mathematics. [Lee, Hirsch and Crawford, 2019](https://pubs.usgs.gov/publication/sir20195084) and [Zhang and Hirsch, 2019](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019WR025338). WRTDS_K is based on this simple idea. On any given sampled day, the measured concentration will almost certainly differ from the value that WRTDS would predict. The measured value is always a better value to use for that day than is the WRTDS estimate for that day. Furthermore, days surrounding the day of measurement are likely to have values not too different from the sampled day. For any given day, the WRTDS_K method uses a mixture of the WRTDS model’s estimated value and the measured value on the nearest sampled day prior and nearest sampled day after the given day to create an optimal estimate for that day. The mathematics of the method uses an auto-regressive, lag-one day, stochastic model to make these estimates of concentration (and hence flux) for each day in the record. @@ -93,7 +93,7 @@ This function produces annual flow-normalized concentrations and fluxes for the As discussed above, GFN is designed to account for the role of trends in discharge in the analysis of trends in concentration or flux. See [GFN approach](https://doi-usgs.github.io/EGRET/articles/Enhancements.html#generalized-flow-normalization-gfn) for details about how it is implemented. The discussion of the motivations and the mathematics of it are in [Choquette, et al. (2019)](https://doi.org/10.1016/j.jglr.2018.11.012). When GFN is used, any trends that are reported are partitioned into two components. One is the concentration versus discharge trend component (CQTC). The other is the discharge trend component (QTC). The two components are additive, and sum to the total trend. In the case of SFN the QTC is defined as equal to zero and thus the entire trend can be considered the QCTC. -One particular thing to note about implementing GFN is that in preparing the data for use in the analysis it is valuable to make use of the daily discharge for many years both before and after the period of water quality data, to the extent possible. This is different from how the data should be prepared for SFN, where it is suggested that the daily discharge record extend no more than a year before the first water quality sample and no more than a year after the last water quality sample. The functions used for GFN are the same three functions mentioned above: `runPairs()`, `runGroups()`, and `runSeries()`. Each of these functions contains arguments that are used to implement GFN. Uncertainty analysis for these functions can be found in EGRETci in the functions [EGRETci::runPairsBoot()](https://doi-usgs.github.io/EGRETci/reference/runPairsBoot.htmll), [EGRETci::runGroupsBoot()](https://doi-usgs.github.io/EGRETci/reference/runGroupsBoot.html), and [EGRETci::ciCalculations()](https://doi-usgs.github.io/EGRETci/reference/ciCalculations.html). +One particular thing to note about implementing GFN is that in preparing the data for use in the analysis it is valuable to make use of the daily discharge for many years both before and after the period of water quality data, to the extent possible. This is different from how the data should be prepared for SFN, where it is suggested that the daily discharge record extend no more than a year before the first water quality sample and no more than a year after the last water quality sample. The functions used for GFN are the same three functions mentioned above: `runPairs()`, `runGroups()`, and `runSeries()`. Each of these functions contains arguments that are used to implement GFN. Uncertainty analysis for these functions can be found in EGRETci in the functions [EGRETci::runPairsBoot()](https://doi-usgs.github.io/EGRETci/reference/runPairsBoot.html), [EGRETci::runGroupsBoot()](https://doi-usgs.github.io/EGRETci/reference/runGroupsBoot.html), and [EGRETci::ciCalculations()](https://doi-usgs.github.io/EGRETci/reference/ciCalculations.html). ### 5c. Generalized flow normalization with a flow break. From c0ae617054d1cc90e8ea9b9602d4a13324a0d503 Mon Sep 17 00:00:00 2001 From: Laura A DeCicco Date: Tue, 5 Nov 2024 08:40:51 -0600 Subject: [PATCH 2/2] Better links --- README.Rmd | 4 ++-- README.md | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.Rmd b/README.Rmd index c28dd93f..674ee361 100644 --- a/README.Rmd +++ b/README.Rmd @@ -26,13 +26,13 @@ Exploration and Graphics for RivEr Trends (`EGRET`): An R-package for the analysis of long-term changes in water quality and streamflow, including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS). Look for new and improved documentation here: - + The link for the official USGS publication user guide is here: [https://pubs.usgs.gov/tm/04/a10/](https://pubs.usgs.gov/tm/04/a10/) -A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. +A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci/) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. If you are familiar with the traditional `EGRET` workflow, check out the [Overview and Updates](https://doi-usgs.github.io/EGRET/articles/Overview.html to see how all the latest updates relate. diff --git a/README.md b/README.md index f7f0175b..2b0a8172 100644 --- a/README.md +++ b/README.md @@ -11,13 +11,13 @@ including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS). Look for new and improved documentation here: - + The link for the official USGS publication user guide is here: -A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci) +A companion package [`EGRETci`](https://doi-usgs.github.io/EGRETci/) implements a set of approaches to the analysis of uncertainty associated with WRTDS trend analysis. @@ -462,7 +462,7 @@ sessioninfo::session_info() #> digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1) #> dotCall64 1.2 2024-10-04 [1] CRAN (R 4.4.1) #> e1071 1.7-16 2024-09-16 [1] CRAN (R 4.4.1) -#> EGRET * 3.0.10 2024-11-01 [1] local +#> EGRET * 3.0.10 2024-11-05 [1] local #> evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1) #> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0) #> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)