Statistical Methods for the Sujatha Distribution

Hosenur Rahman Prodhani

2026-08-23

1 Introduction

The Sujatha distribution, introduced by Shanker (2016), is a one-parameter lifetime distribution that has been successfully applied to lifetime, reliability, biological and engineering data. The SujathaDist package provides computational tools for distributional analysis, random number generation, parameter estimation and goodness-of-fit assessment.

2 Installation install.packages(“remotes”)

remotes::install_github(“hosenur72/SujathaDist”)

3 Loading the package library(SujathaDist)

4 Distribution functions

Explain

Density Distribution Quantile Random generation

Example

theta <- 2

dsujatha(1, theta)

psujatha(1, theta)

qsujatha(.5, theta)

rsujatha(10, theta)

5 Visualization

Include plots such as:

curve(dsujatha(x,2), from=0, to=10, lwd=2)

and

curve(psujatha(x,2), from=0, to=10, lwd=2)

6 Random sample generation set.seed(123)

x <- rsujatha(1000,2)

summary(x) 7 Parameter estimation fit <- fitsujatha(x)

summary(fit)

Explain:

estimate standard error confidence interval

8 Goodness-of-fit gofsujatha(fit)

Discuss:

Log-likelihood AIC BIC KS statistic p-value

9 Complete example

Provide a workflow from simulation to model fitting:

set.seed(123)

x <- rsujatha(100)

fit <- fitsujatha(x)

summary(fit)

gofsujatha(fit)

10 References

Include:

Prodhani, H. R. (2026). SujathaDist: Statistical Methods for the Sujatha Distribution. R package version 1.0.0. Shanker, R. (2016). Sujatha Distribution and its Applications. Statistics in Transition New Series, 17(3), 391–410.