# expWeight: This function computes the smooth windowing weights In SmoothWin: Soft Windowing on Linear Regression

## Description

This function computes the exponential weights (smooth windowing function) for different shapes (k) and bandwidth (l) and provides some plots

## Usage

 `1` ``` expWeight(t, k, l, m = 0, plot = FALSE, ...) ```

## Arguments

 `t` Vector. a vector of positive continuous values `k` A single value for sharpness `l` A single value for bandwidth `m` Vector. The location of the modes on t (modes are the peak of the windows) `plot` Logical flag. Set to true shows a plot of the weights. `...` The parameters that can be passed to the plot() function

## Value

A vector of weights

## Author(s)

`SmoothWin`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40``` ``` par(mfrow = c(3, 1)) # Example 1 - no merging in windows weight = expWeight( t = 1:100 , k = 5 , l = 10 , m = c(25, 50, 75) , plot = TRUE , ### Passed parameters to the plot function type = 'l' , lty = 2 , lwd = 3 , main = 'If windows do not intersect, then wont merge!' ) # Example 2 - merging in windows weight = expWeight( t = 1:100 , k = 5 , l = 15 , m = c(25, 50, 75) , plot = TRUE , ### Passed parameters to the plot function type = 'l' , lty = 2 , lwd = 3 , main = 'If windows intersect, then merge!' ) # Example 3 - partial merging in windows weight = expWeight( t = 1:100 , k = 1 , l = 12 , m = c(25, 50, 75) , plot = TRUE , ### Passed parameters to the plot function type = 'l' , lty = 2 , lwd = 3 , main = 'If windows intersect with small k, then partially merge!' ) ```