triangle_mle: Maximum likelihood estimate of the triangle distribution...

In bertcarnell/triangle: Distribution Functions and Parameter Estimates for the Triangle Distribution

Maximum likelihood estimate of the triangle distribution parameters

Description

Maximum likelihood estimate of the triangle distribution parameters

Usage

triangle_mle(x, debug = FALSE, maxiter = 100, boot_var = FALSE, boot_rep = 500)

Arguments

x	sample from a triangle distribution
debug	if TRUE then the function will check the input parameters and print calculation information
maxiter	the maximum number of cycles of optimization between maximizing a and b given c and maximizing c given a and b
boot_var	should the variance be computed with a boostrap sample?
boot_rep	The number of boostrap replications

Value

an object of S3 class triangle_mle containing a list with the call, coefficients, variance co-variance matrix, minimum negative log likelihood, details of the optimization number of observations, and the sample

References

Samuel Kotz and Johan Rene van Dorp. Beyond Beta \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1142/5720")}

Examples

xtest <- c(0.1, 0.25, 0.3, 0.4, 0.45, 0.6, 0.75, 0.8)
triangle_mle(xtest)

xtest <- rtriangle(20, 1, 5, 3.5)
triangle_mle(xtest)

bertcarnell/triangle documentation built on July 4, 2025, 12:41 a.m.