R Under development (unstable) (2023-11-06 r85483 ucrt) -- "Unsuffered Consequences"
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> library("matrixStats")
> 
> x <- 1:5
> y <- weightedMedian(x)
> y <- weightedMedian(x, w = c(NA, Inf, NA, Inf, NA), na.rm = TRUE)
> print(y)
[1] 3
> 
> y <- weightedMedian(x, w = c(NA, Inf, NA, Inf, NA), na.rm = FALSE)
> print(y)
[1] NA
> stopifnot(is.na(y))
> 
> x <- 1:10
> n <- length(x)
> 
> y1 <- median(x)                           # 5.5
> y2 <- weightedMedian(x)                   # 5.5
> stopifnot(all.equal(y1, y2))
> 
> 
> w <- rep(1, times = n)
> y1 <- weightedMedian(x, w)                 # 5.5 (default)
> y2a <- weightedMedian(x, ties = "weighted")  # 5.5 (default)
> y2b <- weightedMedian(x, ties = "min")       # 5
> y2c <- weightedMedian(x, ties = "max")       # 6
> stopifnot(all.equal(y2a, y1))
> 
> y3 <- weightedMedian(x, w)                # 5.5 (default)
> 
> 
> # Pull the median towards zero
> w[1] <- 5
> y1 <- weightedMedian(x, w)                # 3.5
> y <- c(rep(0, times = w[1]), x[-1])       # Only possible for integer weights
> y2 <- median(y)                           # 3.5
> stopifnot(all.equal(y1, y2))
> 
> # Put even more weight on the zero
> w[1] <- 8.5
> y <- weightedMedian(x, w)                # 2
> 
> # All weight on the first value
> w[1] <- Inf
> y <- weightedMedian(x, w)                # 1
> 
> # All weight on the last value
> w[1] <- 1
> w[n] <- Inf
> y <- weightedMedian(x, w)                # 10
> 
> # All weights set to zero
> w <- rep(0, times = n)
> y <- weightedMedian(x, w)                # NA
> 
> x <- 1:4
> w <- rep(1, times = 4)
> for (mode in c("integer", "double")) {
+   storage.mode(x) <- mode
+   for (ties in c("weighted", "mean", "min", "max")) {
+     cat(sprintf("ties = %s\n", ties))
+     y <- weightedMedian(x, w, ties = ties)
+   }
+ }
ties = weighted
ties = mean
ties = min
ties = max
ties = weighted
ties = mean
ties = min
ties = max
> 
> set.seed(0x42)
> 
> y <- weightedMedian(x = double(0L))
> print(y)
[1] NA
> stopifnot(length(y) == 1L)
> stopifnot(is.na(y))
> 
> y <- weightedMedian(x = x[1])
> print(y)
[1] 1
> stopifnot(length(y) == 1L)
> stopifnot(all.equal(y, x[1]))
> 
> 
> n <- 1e3
> x <- runif(n)
> w <- runif(n, min = 0, max = 1)
> for (mode in c("integer", "double")) {
+   storage.mode(x) <- mode
+   for (ties in c("weighted", "mean", "min", "max")) {
+     y <- weightedMedian(x, w, ties = ties)
+     cat(sprintf("mode = %s, ties = %s, result = %g\n", mode, ties, y))
+   }
+ }
mode = integer, ties = weighted, result = 0
mode = integer, ties = mean, result = 0
mode = integer, ties = min, result = 0
mode = integer, ties = max, result = 0
mode = double, ties = weighted, result = 0
mode = double, ties = mean, result = 0
mode = double, ties = min, result = 0
mode = double, ties = max, result = 0
> 
> 
> # A large vector
> n <- 1e5
> x <- runif(n)
> w <- runif(n, min = 0, max = 1)
> y <- weightedMedian(x, w)
> 
> y <- weightedMedian(x, w, ties = "min")
> 
> 
> # Single Number
> xs <- c(1, NA_integer_)
> ws <- c(1, NA_integer_)
> for (x in xs) {
+   for (w in ws) {
+     y <- weightedMedian(x = x, w = w)
+     if (is.na(w)) z <- NA_real_
+     else z <- x[1]
+     stopifnot(all.equal(y, z))
+   }
+ }
> 
> ## Logical
> x1 <- c(TRUE, FALSE, TRUE)
> w0 <- c(0, 0, 0)
> stopifnot(!is.finite(weightedMedian(x1, w0)),
+           !is.infinite(weightedMedian(x1, w0)))
> 
> w1 <- c(1, 1, 1)
> stopifnot(weightedMedian(x1, w1) == 1)
> 
> w2 <- c(1, 2, 3)
> stopifnot(weightedMedian(x1, w2) == 1)
> 
> ### NA
> stopifnot(is.na(weightedMedian(c(TRUE, FALSE, NA),
+                                c(1, 2, 3))),
+           all.equal(weightedMedian(c(TRUE, FALSE, NA),
+                                    c(1, 2, 3),
+                                    na.rm = TRUE),
+                     weightedMedian(c(TRUE, FALSE),
+                                    c(1, 2))))
> ### Identical to as.integer()
> x <- rcauchy(100)
> w <- abs(rcauchy(100))
> stopifnot(all.equal(weightedMedian(x > 0, w),
+                     weightedMedian(as.integer(x > 0), w)))
> 
> 
> 
> proc.time()
   user  system elapsed 
   0.18    0.06    0.25