Distribution fitting using various criteria in R

Question

Consider me to be an undergraduate student doing a routine research study.

Is there a simple, straight forward way to find and access (or implement) distribution fitting in R using the following estimates:

• Kolmogorov-Smirnov Minimum Distance estimate
• Cramér–von Mises Minimum Distance estimate
• Anderson-Darling Minimum Distance estimate
• Maximum Likelihood Estimate

I got my self lost in tons of documentation and reference manuals on various R packages.

The question more relates to using R software system than to statistics itself, that's why I asked it here, at SO.

Answer 1

library(fitdistrplus)

# normally distributed sample
x1 = rnorm(100)

# Kolmogorov-Smirnov mimimum distance method
fitdist(x1, "mge", distr="norm", gof="KS")

# Cramer-von Mises
fitdist(x1, "mge", distr="norm", gof="CvM")

# Anderson-Darling
fitdist(x1, "mge", distr="norm", gof="AD")

# Maximum Likelihood estimate
fitdist(x1, "mle", distr="norm")

Things are appeared to be pretty straight forward.