add news item and quantile type

Author: rsbivand

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: classInt
-Version: 0.4-7
-Date: 2022-06-10
+Version: 0.4-8
+Date: 2022-09-22
 Title: Choose Univariate Class Intervals
 Authors@R: c(
   person("Roger", "Bivand", role=c("aut", "cre"), email="[email protected]", comment=c(ORCID="0000-0003-2392-6140")),

NEWS.md

@@ -1,3 +1,7 @@
+## Version 0.4-8
+
+- #18 and #38: `classIntervals()` has a new style `"box"`, where a box map is an augmented quartile map, with an additional lower and upper category. When there are lower outliers, then the starting point for the breaks is the minimum value, and the second break is the lower fence. When there are no lower outliers, then the starting point for the breaks will be the lower fence, and the second break is the minimum value (there will be no observations that fall in the interval between the lower fence and the minimum value) (@angela-li, @dieghernan).
+
 ## Version 0.4-7
 
 - A new helper function `classify_intervals()` is introduced to return a vector of class intervals of same length as input (@JosiahParry)

R/classInt.R

@@ -357,8 +357,9 @@ classIntervals <- function(var, n, style="quantile", rtimes=3, ..., intervalClos
 
         dots <- list(...)
         iqr_mult <- ifelse(is.null(dots$iqr_mult), 1.5, dots$iqr_mult)
+        qtype <- ifelse(is.null(dots$type), 7, dots$type)
 
-        qv <- unname(quantile(var))
+        qv <- unname(quantile(var, type=qtype))
         iqr <- iqr_mult * (qv[4] - qv[2])
         upfence <- qv[4] + iqr
         lofence <- qv[2] - iqr

man/classIntervals.Rd

@@ -70,11 +70,7 @@ classIntervals2shingle(x)
 
   The "maximum" style uses the Maximum Breaks method of classification finding the k - 1 largest differences in \code{var}. The mean of the values that generated the largest splits is used as the interval boundary.
   
-  The "box" style generate 7 breaks (therefore 6 categories) based on a box-and-whisker plot. First and last categories 
-  includes the data values considered as outliers, and the four remaining categories are defined by the percentiles 25, 
-  50 and 75 of the data distribution. By default, the identification of outliers is based on the interquantile range 
-  (IQR), so values lower than percentile 25 - 1.5 * IQR or higher than percentile 75 + 1.5 * IQR are considered as outliers. 
-  The multiplier applied to the IQR \code{iqr_mult = 1.5} may be modified through \code{\dots}.
+  The "box" style generates 7 breaks (therefore 6 categories) based on a box-and-whisker plot. First and last categories include the data values considered as outliers, and the four remaining categories are defined by the percentiles 25, 50 and 75 of the data distribution. By default, the identification of outliers is based on the interquantile range (IQR), so values lower than percentile 25 - 1.5 * IQR or higher than percentile 75 + 1.5 * IQR are considered as outliers. The multiplier applied to the IQR \code{iqr_mult = 1.5} may be modified through \code{\dots}. As in the \code{"quantile"} style, the \code{type=} argument may be used to choose the quantile algoritm (default 7).
 
 }