Some bugfixes and more flexible concentration lengths

description

• Fixed unintended behaviour in plot_effect() and plot_stress() where supplying an empty vector caused the four standard curves to show. Now setting which to an empty vector or NULL shows just the axes. The default value is NA.
• Renamed the mixture_effect column in the predict_mixture output data frame to effect.
• Remove the restriction that the concentration vectors in predict_mixture() must be the same length. The longer length must be a multiple of the shorter length because the shorter vector gets recycled to the longer length.
• Modify gitlab_ci to give verbose package installation output. And remove the check for the previous R version because somehow drc and plotrix are not available there.

system details

• RStudio Version: 1.2.5033
• R Version: 3.6.3
• OS Version: Windows 10 x64 (build 18363)

checklist

• This merge introduces new features.
  • new features are documented
  • new features have tests
• update documentation including examples (if necessary)
• update and polish NEWS.md
• update version in DESCRIPTION
• update date in DESCRIPTION
• no remaining TODO, FIXME, or debug prints anywhere in the source files
• devtools::document()
• devtools::check() without errors or warnings

R/predict_mixture.R

@@ -31,7 +31,9 @@
 #'
 #' @param model_a,model_b The ecxsys models of the toxicants.
 #' @param concentration_a,concentration_b The concentrations of the toxicants in
-#'   the mixture. Both vectors must be the same length.
+#'   the mixture. Both vectors must either be the same length or the longer
+#'   length must be a multiple of the shorter length. That's because the shorter
+#'   concentration vector gets recycled to the length of the longer one.
 #' @param proportion_ca The proportion of concentration addition in the
 #'   calculation of the toxicant stress of the mixture. Must be between 0 and 1.
 #' @param effect_max Controls the scaling of the result. This represents the
@@ -61,7 +63,7 @@
 #'
 #' # Example of symmetric prediction:
 #' conc_a <- c(0, 0.03, 0.3, 3)
-#' conc_b <- rep(5.5, 4)
+#' conc_b <- 5.5
 #' prop_ca <- 0.75
 #' mix_a <- predict_mixture(toxicant_a , toxicant_b , conc_a, conc_b, prop_ca)
 #' mix_b <- predict_mixture(toxicant_b , toxicant_a , conc_b, conc_a, prop_ca)
@@ -81,7 +83,6 @@ predict_mixture <- function(model_a,
         is.numeric(concentration_b),
         length(concentration_a) > 0,
         length(concentration_b) > 0,
-        length(concentration_a) == length(concentration_b),
         all(!is.na(concentration_a)),
         all(!is.na(concentration_b)),
         proportion_ca >= 0,