```
x <- Rle(1:10, 1:10)
runsum(x, k = 3)
runsum(x, k = 3, endrule = "constant")
runmean(x, k = 3)
runwtsum(x, k = 3, wt = c(0.25, 0.5, 0.25))
runq(x, k = 5, i = 3, endrule = "constant")
## Missing and non-finite values
x <- Rle(c(1, 2, NA, 0, 3, Inf, 4, NaN))
runsum(x, k = 2)
runsum(x, k = 2, na.rm = TRUE)
runmean(x, k = 2, na.rm = TRUE)
runwtsum(x, k = 2, wt = c(0.25, 0.5), na.rm = TRUE)
runq(x, k = 2, i = 2, na.rm = TRUE) ## max value in window
## The .naive_runsum() function demonstrates the semantics of
## runsum(). This test ensures the behavior is consistent with
## base::sum().
.naive_runsum <- function(x, k, na.rm=FALSE)
sapply(0:(length(x)-k),
function(offset) sum(x[1:k + offset], na.rm=na.rm))
x0 <- c(1, Inf, 3, 4, 5, NA)
x <- Rle(x0)
target1 <- .naive_runsum(x0, 3, na.rm = TRUE)
target2 <- .naive_runsum(x, 3, na.rm = TRUE)
stopifnot(target1 == target2)
current <- as.vector(runsum(x, 3, na.rm = TRUE))
stopifnot(target1 == current)
## runmean() and runwtsum() :
x <- Rle(c(2, 1, NA, 0, 1, -Inf))
runmean(x, k = 3)
runmean(x, k = 3, na.rm = TRUE)
runwtsum(x, k = 3, wt = c(0.25, 0.50, 0.25))
runwtsum(x, k = 3, wt = c(0.25, 0.50, 0.25), na.rm = TRUE)
## runq() :
runq(x, k = 3, i = 1, na.rm = TRUE) ## smallest value in window
runq(x, k = 3, i = 3, na.rm = TRUE) ## largest value in window
## When na.rm = TRUE, it is possible the number of non-NA
## values in the window will be less than the 'i' specified.
## Here we request the 4th smallest value in the window,
## which tranlates to the value at the 4/5 (0.8) percentile.
x <- Rle(c(1, 2, 3, 4, 5))
runq(x, k=length(x), i=4, na.rm=TRUE)
## The same request on a Rle with two missing values
## finds the value at the 0.8 percentile of the vector
## at the new length of 3 after the NA's have been removed.
## This translates to round((0.8) * 3).
x <- Rle(c(1, 2, 3, NA, NA))
runq(x, k=length(x), i=4, na.rm=TRUE)
```

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