# moments: Moments In metinbulus/cosa: Bound Constrained Optimal Sample Size Allocation

## Description

If data (vector) is provided use `emp.moment()` function, otherwise for truncated normal distribution use `tnorm.moment()`, and for uniform distribution use `unif.moment()`.

## Usage

 ```1 2 3``` ``` tnorm.moment(mu = 0, sigma = 1, k1 = -Inf, k2 = Inf, order = 1, central = FALSE) unif.moment(k1 = 0, k2 = 1, order = 1, central = FALSE) emp.moment(x, order = 1, central = FALSE, absolute = FALSE, na.rm = FALSE) ```

## Arguments

 `mu` mean of truncated normal - applies to `tnorm.moment()`. `sigma` standard deviation of truncated normal - applies to `tnorm.moment()`. `k1` left truncation point for truncated normal distribution or lower bound for uniform distribution. `k2` right truncation point for truncated normal distribution or upper bound for uniform distribution. `order` + int; order of moment `x` a vector of values - applies to `emp.moment()`. `central` logical; if `TRUE` produces central moments. `absolute` logical; if `TRUE` produces absolute moments - applies to `emp.moment()`. `na.rm` logical; if `TRUE` removes missing values - applies to `emp.moment()`.

## Examples

 ```1 2 3 4``` ```tnorm.moment(k1 = -20, k2 = 20, order = 4, central = FALSE) emp.moment(rnorm(10000), order = 4, central = FALSE) unif.moment(k1 = 0, k2 = 1, order = 4, central = FALSE) emp.moment(runif(10000), order = 4, central = FALSE) ```

metinbulus/cosa documentation built on Sept. 9, 2021, 12:04 p.m.