docs: add keywords to references

Author: atsyplenkov

R/RcppExports.R

@@ -1,27 +1,27 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-kge_cpp <- function(obs, sim, na_rm = TRUE, version = "2012") {
-    .Call(`_tidyhydro_kge_cpp`, obs, sim, na_rm, version)
-}
-
-mse_cpp <- function(truth, estimate, na_rm = TRUE) {
-    .Call(`_tidyhydro_mse_cpp`, truth, estimate, na_rm)
-}
-
-nse_cpp <- function(truth, estimate, performance = FALSE, na_rm = TRUE) {
-    .Call(`_tidyhydro_nse_cpp`, truth, estimate, performance, na_rm)
-}
-
-pbias_cpp <- function(truth, estimate, performance = FALSE, na_rm = TRUE) {
-    .Call(`_tidyhydro_pbias_cpp`, truth, estimate, performance, na_rm)
-}
-
-press_cpp <- function(truth, estimate, na_rm = TRUE) {
-    .Call(`_tidyhydro_press_cpp`, truth, estimate, na_rm)
-}
-
-sfe_cpp <- function(truth, estimate, na_rm = TRUE) {
-    .Call(`_tidyhydro_sfe_cpp`, truth, estimate, na_rm)
-}
-
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+kge_cpp <- function(obs, sim, na_rm = TRUE, version = "2012") {
+    .Call(`_tidyhydro_kge_cpp`, obs, sim, na_rm, version)
+}
+
+mse_cpp <- function(truth, estimate, na_rm = TRUE) {
+    .Call(`_tidyhydro_mse_cpp`, truth, estimate, na_rm)
+}
+
+nse_cpp <- function(truth, estimate, performance = FALSE, na_rm = TRUE) {
+    .Call(`_tidyhydro_nse_cpp`, truth, estimate, performance, na_rm)
+}
+
+pbias_cpp <- function(truth, estimate, performance = FALSE, na_rm = TRUE) {
+    .Call(`_tidyhydro_pbias_cpp`, truth, estimate, performance, na_rm)
+}
+
+press_cpp <- function(truth, estimate, na_rm = TRUE) {
+    .Call(`_tidyhydro_press_cpp`, truth, estimate, na_rm)
+}
+
+sfe_cpp <- function(truth, estimate, na_rm = TRUE) {
+    .Call(`_tidyhydro_sfe_cpp`, truth, estimate, na_rm)
+}
+

R/data.R

@@ -1,5 +1,6 @@
 #' Mean Daily Water Discharge At Avacha River (Elizovo City)
-#'
+#' @keywords data
+#' 
 #' @details These data contain the measured (`obs`) mean daily water discharge
 #' values (in \eqn{m^3/s}) at the Avacha River -- Elizovo City state gauging
 #' station for the 2022 calendar year. They are accompanied by the GloFAS v4.0

R/kge.R

@@ -1,4 +1,5 @@
 #' Kling-Gupta Efficiency (KGE)
+#' @keywords gof
 #'
 #' @description
 #' Calculate the Kling-Gupta Efficiency (*Gupta et al., 2009*).
@@ -122,6 +123,7 @@ kge_vec <- function(
 }
 
 #' Modified Kling-Gupta Efficiency (KGE')
+#' @keywords gof
 #'
 #' @description
 #' Calculate the modified Kling-Gupta Efficiency (*Kling et al., 2012*),

R/mse.R

@@ -1,4 +1,5 @@
 #' Mean Squared Error (MSE)
+#' @keywords gof
 #'
 #' @description
 #' The MSE is a metric that evaluates the goodness of fit between model

R/nse.R

@@ -1,4 +1,5 @@
 #' Nash-Sutcliffe Efficiency (NSE)
+#' @keywords gof
 #'
 #' @description
 #' Calculate the Nash-Sutcliffe efficiency (*Nash & Sutcliffe, 1970*).

R/pbias.R

@@ -1,12 +1,13 @@
 #' Percent BIAS (pBIAS)
+#' @keywords gof
 #'
 #' @description
-#' \eqn{pBIAS} is the deviation of data being evaluated, expressed as a 
-#' percentage. It measures the average tendency of the simulated data to be 
-#' larger or smaller than their observed counterparts (*Moriasi et al., 2015*). 
-#' The optimal value of \eqn{pBIAS} is 0.0, with low-magnitude values 
-#' indicating accurate mode simulation. Positive values indicate model 
-#' underestimation bias, and negative values indicate model overestimation 
+#' \eqn{pBIAS} is the deviation of data being evaluated, expressed as a
+#' percentage. It measures the average tendency of the simulated data to be
+#' larger or smaller than their observed counterparts (*Moriasi et al., 2015*).
+#' The optimal value of \eqn{pBIAS} is 0.0, with low-magnitude values
+#' indicating accurate mode simulation. Positive values indicate model
+#' underestimation bias, and negative values indicate model overestimation
 #' bias (*Gupta et al., 1999*).
 #'
 #' @details
@@ -16,13 +17,13 @@
 #'   pBIAS = 100 \times \frac{\sum_{i=1}^{n}{(sim_i - obs_i)}}
 #'                           {\sum_{i=1}^{n}{obs_i}}
 #' }
-#' 
+#'
 #' where:
 #' \itemize{
 #'   \item \eqn{sim} defines model simulations at time step \eqn{i}
 #'   \item \eqn{obs} defines model observations at time step \eqn{i}
 #' }
-#' 
+#'
 #' According to Moriasi et al. (2015) the metric interpretation can be as
 #' follows:
 #'

R/press.R

@@ -1,4 +1,5 @@
 #' PRediction Error Sum of Squares (PRESS)
+#' @keywords regression
 #'
 #' @description
 #' \eqn{PRESS} is a measure of the quality of a regression model using

R/sfe.R

@@ -1,4 +1,5 @@
 #' Standard Factorial Error (SFE)
+#' @keywords regression
 #'
 #' @description
 #' Prediction standard factorial error estimated
@@ -7,8 +8,8 @@
 #' @details
 #' The metric is widely used for assessing Sediment Rating Curves
 #' (e.g., Hicks et al. 2020). The model is usually considered 'unacceptable'
-#' if the \eqn{SFE > 2}, see Hicks et al. (2011). 
-#' 
+#' if the \eqn{SFE > 2}, see Hicks et al. (2011).
+#'
 #' It is estimated as follows:
 #' \deqn{SFE = \exp\left(\sqrt{\frac{1}{n} \sum_{i=1}^{n}
 #' \left( \log\left(\frac{obs_i}{sim_i} \right) \right)^2 }\right)}

README.Rmd

@@ -38,13 +38,12 @@ The `tidyhydro` package provides a set of commonly used metrics in hydrology (su
 Additionally, you'll find here a C++ implementation of lesser-known yet powerful metrics used in reports from the United States Geological Survey (USGS) and the National Environmental Monitoring Standards (NEMS) guidelines. Examples include _PRESS_ (Prediction Error Sum of Squares), _SFE_ (Standard Factorial Error), and _MSPE_ (Model Standard Percentage Error) and others. Based on the equations from _Helsel et al._ ([2020](https://pubs.usgs.gov/publication/tm4A3)), _Rasmunsen et al._ ([2008](https://pubs.usgs.gov/tm/tm3c4/)), _Hicks et al._ ([2020](https://www.nems.org.nz/documents/suspended-sediment)) and etc. (see documentation for details).
 
 ## Example
-The `tidyhydro` package follows the philosophy of [`yardstick`](https://github.com/tidymodels/yardstick/tree/main) and provides S3 class methods for vectors and data frames. For example, one can estimate `NSE` and `pBIAS` for a data frame like this:
+The `tidyhydro` package follows the philosophy of [`yardstick`](https://github.com/tidymodels/yardstick/tree/main) and provides S3 class methods for vectors and data frames. For example, one can estimate `KGE`, `NSE` or `pBIAS` for a data frame like this:
 
 ```{r example}
 library(tidyhydro)
 data(avacha)
-
-avacha
+str(avacha)
 
 kge(avacha, obs, sim)
 ```

README.md

@@ -41,27 +41,16 @@ Error) and others. Based on the equations from *Helsel et al.*
 The `tidyhydro` package follows the philosophy of
 [`yardstick`](https://github.com/tidymodels/yardstick/tree/main) and
 provides S3 class methods for vectors and data frames. For example, one
-can estimate `NSE` and `pBIAS` for a data frame like this:
+can estimate `KGE`, `NSE` or `pBIAS` for a data frame like this:
 
 ``` r
 library(tidyhydro)
 data(avacha)
-
-avacha
-#> # A data frame: 365 × 3
-#>    date         obs   sim
-#>    <date>     <dbl> <dbl>
-#>  1 2022-01-01  76.2  84.8
-#>  2 2022-01-02  76.2  84.3
-#>  3 2022-01-03  76.3  84.0
-#>  4 2022-01-04  76.3  83.7
-#>  5 2022-01-05  76.4  83.4
-#>  6 2022-01-06  76.4  83.1
-#>  7 2022-01-07  76.5  83.0
-#>  8 2022-01-08  76.5  82.9
-#>  9 2022-01-09  76.6  82.8
-#> 10 2022-01-10  76.6  82.7
-#> # ℹ 355 more rows
+str(avacha)
+#> Classes 'tbl_df', 'tbl' and 'data.frame':    365 obs. of  3 variables:
+#>  $ date: Date, format: "2022-01-01" "2022-01-02" ...
+#>  $ obs : num  76.2 76.2 76.3 76.3 76.4 76.4 76.5 76.5 76.6 76.6 ...
+#>  $ sim : num  84.8 84.3 84 83.7 83.4 ...
 
 kge(avacha, obs, sim)
 #> # A tibble: 1 × 3
@@ -140,9 +129,9 @@ bench::mark(
 #> # A tibble: 3 × 6
 #>   expression   min median `itr/sec` mem_alloc `gc/sec`
 #>   <bch:expr> <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
-#> 1 tidyhydro    1      1       52.8        NaN      NaN
-#> 2 hydroGOF    30.5   41.1      1          Inf      Inf
-#> 3 baseR       17.3   20.6      2.31       Inf      Inf
+#> 1 tidyhydro   1       1       29.2        NaN      NaN
+#> 2 hydroGOF   15.1    19.1      1          Inf      Inf
+#> 3 baseR       8.47   10.4      2.42       Inf      Inf
 ```
 
 ## Code of Conduct