v1.1.0 to CRAN

Author: fawda123

.Rbuildignore

@@ -3,6 +3,8 @@
 ^data-raw$
 README.Rmd
 README.md
+README.html
+^README_FILES/.*
 cran-comments.md
 .gitignore
 .Rhistory

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: WRTDStidal
 Type: Package
 Title: Weighted Regression for Water Quality Evaluation in Tidal Waters
-Version: 1.0.2.9000
-Date: 2016-12-08
+Version: 1.1.0
+Date: 2017-06-23
 Author: Marcus W. Beck [aut, cre]
 Maintainer: Marcus W. Beck <[email protected]>
 Description: An adaptation for estuaries (tidal waters) of weighted regression
@@ -34,4 +34,4 @@ Depends:
 Authors@R: person(given = "Marcus W.", family = "Beck",
     role = c("aut","cre"),
     email = "[email protected]")
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1

R/all_sims.R

@@ -11,7 +11,7 @@
 #' 
 #' @export
 #' 
-#' @seealso \code{\link{daydat}} for example data, \code{\link{lnQ_sim}} for simulating discharge, \code{\link{lnres_err}} for estimating the eror structure of the response variable, and \code{\link{lnres_sim}} for simulating the response variable
+#' @seealso \code{\link{daydat}} for example data, \code{\link{lnQ_sim}} for simulating discharge, \code{\link{lnres_err}} for estimating the error structure of the response variable, and \code{\link{lnres_sim}} for simulating the response variable
 #' 
 #' @import dplyr
 #' 

R/createsrch.R

@@ -1,6 +1,6 @@
 #' Create a grid of half-window widths to evaluate
 #' 
-#' Create a grid of all unique combintations of half-window widths to evaluate.  The result can be passed to \code{\link{winsrch_grid}}.
+#' Create a grid of all unique combinations of half-window widths to evaluate.  The result can be passed to \code{\link{winsrch_grid}}.
 #' 
 #' @param mos numeric vector of half-window widths for months, a value of one indicates twelve months
 #' @param yrs numeric vector of half-window widths for years, a value of one indicates one-year

R/dec_time.R

@@ -8,7 +8,7 @@
 #' 
 #' @export
 #' 
-#' @return A named list of four numeric vecors including \code{day_num} (decimal day on an annual scale), \code{month} (month of the year as integer), \code{year}, and \code{dec_time} (decimal time as sum of \code{year} and \code{day_num})
+#' @return A named list of four numeric vectors including \code{day_num} (decimal day on an annual scale), \code{month} (month of the year as integer), \code{year}, and \code{dec_time} (decimal time as sum of \code{year} and \code{day_num})
 #' 
 #' @examples
 #' dt <- Sys.Date()

R/dynaplot.R

@@ -40,7 +40,7 @@
 #' # plot using defaults, 
 #' # defaults to the fiftieth quantile for all years
 #' dynaplot(tidfit)
-#' 
+#' \dontrun{
 #' # change the defaults
 #' dynaplot(tidfit, tau = 0.9, month = 2, years = seq(1980, 1990), 
 #'  col_vec = rainbow(7), alpha = 0.5, size = 3) 
@@ -49,6 +49,7 @@
 #' data(tidfitmean)
 #' 
 #' dynaplot(tidfitmean)
+#' }
 dynaplot <- function(dat_in, ...) UseMethod('dynaplot')
 
 #' @rdname dynaplot

R/fitmoplot.R

@@ -17,7 +17,7 @@
 #' @param alpha numeric value indicating transparency of points or lines
 #' @param ... arguments passed to other methods
 #' 
-#' @details The plots are similar to those produced by \code{\link{fitplot}} except the values are facetted by month.  This allows an evaluation of trends over time independent of seasonal variation.  Multiple observations within each month for each year are averaged for a smoother plot.
+#' @details The plots are similar to those produced by \code{\link{fitplot}} except the values are faceted by month.  This allows an evaluation of trends over time independent of seasonal variation.  Multiple observations within each month for each year are averaged for a smoother plot.
 #' 
 #' @import dplyr ggplot2 RColorBrewer
 #' 
@@ -34,7 +34,7 @@
 #' 
 #' # plot using defaults
 #' fitmoplot(tidfit)
-#' 
+#' \dontrun{ 
 #' # get the same plot but use default ggplot settings
 #' fitmoplot(tidfit, pretty = FALSE)
 #' 
@@ -45,7 +45,7 @@
 #' fitmoplot(tidfit, predicted = FALSE)
 #' 
 #' # modify the plot as needed using ggplot scales, etc.
-#' 
+#'
 #' library(ggplot2)
 #' 
 #' fitmoplot(tidfit, pretty = FALSE, linetype = 'dashed') + 
@@ -65,6 +65,7 @@
 #' data(tidfitmean)
 #' 
 #' fitmoplot(tidfitmean)    
+#' }
 fitmoplot <- function(dat_in, ...) UseMethod('fitmoplot')
 
 #' @rdname fitmoplot

R/getwts.R

@@ -5,7 +5,7 @@
 #' 
 #' @param dat_in input tidal object
 #' @param ref_in row of tidal object as reference for weights
-#' @param wt_vars chr string of three elements indicatings names of columns in tidal object that are used for reference row weights
+#' @param wt_vars chr string of three elements indicating names of columns in tidal object that are used for reference row weights
 #' @param wins list of half-window widths for time, year, and flow
 #' @param all logical to return individual weights rather than the product of all three, default \code{FALSE}
 #' @param slice logical indicating if data are subset by observations within the maximum window width for faster calculations

R/gradcols.R

@@ -10,7 +10,7 @@
 #' 
 #' @seealso \code{\link{dynaplot}}, \code{\link{gridplot}}, \code{\link{wtsplot}}
 #' 
-#' @return A character vector of colors in hexidecimal notation.
+#' @return A character vector of colors in hexadecimal notation.
 #' 
 #' @examples
 #' 

R/lnres_err.R

@@ -4,7 +4,7 @@
 #'
 #' @param dat_in input \code{\link[base]{data.frame}} that must include discharge and decimal time columns, see example dataset \code{\link{daydat}}
 #' @param yr numeric year value to use for the stationary model, defaults to the median year
-#' @param comps logical indicating ifthe WRTDS model used to get response error measures is also returned, see value.
+#' @param comps logical indicating if the WRTDS model used to get response error measures is also returned, see value.
 #' @param seed optional numeric value for random generation seed
 #' 
 #' @details Random errors for a stationary seasonal water quality time series on a daily time step are generated by modelling residuals from an observed dataset.  First, a stationary seasonal model is created by fitting a \code{\link{wrtds}} model and estimating an error distribution the residuals using the \code{\link[forecast]{auto.arima}} function.  Accumulated standard errors from the regression are also retained for each residual.  Random errors using the estimated auto-regressive structures are simulated using \code{\link[stats]{arima.sim}} for the entire year and multiplied by the corresponding standard error estimate from the regression.  The entire year is then repeated for every year in the observed time series.  The final simulated errors are rescaled to the range of the original residuals that were used to estimate the distribution.