Package {SujathaDist}

Type:	Package
Title:	Statistical Methods for the Sujatha Distribution
Version:	1.0.0
Maintainer:	Hosenur Rahman Prodhani <hosenur72@gmail.com>
Description:	Provides computational tools for the Sujatha distribution, including probability density, cumulative distribution, quantile function, random number generation, parameter estimation by maximum likelihood, goodness-of-fit procedures, and associated statistical methods for lifetime data analysis. The implemented methods are based on the Sujatha distribution proposed by Shanker (2016) <doi:10.59170/stattrans-2016-023>.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.1
Depends:	R (≥ 4.1.0)
Imports:	stats, graphics
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, covr
VignetteBuilder:	knitr
URL:	https://github.com/hosenur72/SujathaDist
BugReports:	https://github.com/hosenur72/SujathaDist/issues
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-08-23 16:53:13 UTC; hosen
Author:	Hosenur Rahman Prodhani [aut, cre]
Repository:	CRAN
Date/Publication:	2026-09-03 12:20:16 UTC

Akaike Information Criterion

Description

Akaike Information Criterion

Usage

## S3 method for class 'fitsujatha'
AIC(object, ...)

Arguments

Value

AIC value.

Bayesian Information Criterion

Description

Returns the Bayesian Information Criterion (BIC).

Usage

## S3 method for class 'fitsujatha'
BIC(object, ...)

Arguments

Value

A numeric value giving the BIC.

Coefficients of a fitted Sujatha distribution

Description

Coefficients of a fitted Sujatha distribution

Usage

## S3 method for class 'fitsujatha'
coef(object, ...)

Arguments

Value

Estimated parameter(s).

Confidence Intervals

Description

Computes Wald confidence intervals.

Usage

## S3 method for class 'fitsujatha'
confint(object, parm = NULL, level = 0.95, ...)

Arguments

Value

Confidence interval matrix.

Deviance

Description

Deviance

Usage

## S3 method for class 'fitsujatha'
deviance(object, ...)

Arguments

Value

Model deviance.

Density Function of the Sujatha Distribution

Description

Computes the probability density function (PDF) of the Sujatha distribution.

Usage

dsujatha(x, theta)

Arguments

Value

A numeric vector of density values.

Examples

dsujatha(1, theta = 2)
dsujatha(c(0.5, 1, 2), theta = 3)

Maximum Likelihood Estimation for the Sujatha Distribution

Description

Fits the Sujatha distribution using Maximum Likelihood Estimation (MLE).

Usage

fitsujatha(x, start = 1, method = "L-BFGS-B")

Arguments

Value

An object of class "fitsujatha" containing

• Estimate

• Standard error

• Variance-covariance matrix

• Hessian matrix

• Log-likelihood

• AIC

• AICc

• BIC

• CAIC

• HQIC

• Deviance

• Convergence status

• Number of iterations

• Original data

Examples

set.seed(123)
x <- rsujatha(100, theta = 2)
fit <- fitsujatha(x)
fit

Goodness-of-Fit Measures for the Sujatha Distribution

Description

Computes several goodness-of-fit statistics for a fitted Sujatha distribution.

Usage

gofsujatha(object)

Arguments

Value

A list containing

• Log-likelihood

• AIC

• AICc

• BIC

• CAIC

• HQIC

• KS statistic

• KS p-value

• Anderson-Darling statistic

• AD p-value

• Cramer-von Mises statistic

• CVM p-value

• Watson statistic

• Watson p-value

Log-likelihood

Description

Log-likelihood

Usage

## S3 method for class 'fitsujatha'
logLik(object, ...)

Arguments

Value

Object of class "logLik".

Diagnostic Plots for Sujatha Distribution

Description

Produces diagnostic plots for an object of class "fitsujatha".

Usage

## S3 method for class 'fitsujatha'
plot(x, which = 1, ...)

Arguments

Value

A diagnostic plot.

Print a fitted Sujatha distribution

Description

Print a fitted Sujatha distribution

Usage

## S3 method for class 'fitsujatha'
print(x, ...)

Arguments

Value

Prints the fitted model summary.

Cumulative Distribution Function of the Sujatha Distribution

Description

Computes the cumulative distribution function (CDF).

Usage

psujatha(q, theta)

Arguments

Value

A numeric vector of cumulative probabilities.

Examples

psujatha(1, theta = 2)

Quantile Function of the Sujatha Distribution

Description

Computes quantiles of the Sujatha distribution by numerically inverting the cumulative distribution function.

Usage

qsujatha(p, theta, lower.tail = TRUE, log.p = FALSE)

Arguments

Value

A numeric vector of quantiles.

Examples

qsujatha(0.5, theta = 2)

Random Generation from the Sujatha Distribution

Description

Generates random observations from the Sujatha distribution using one of three methods: #' @details Three random generation methods are available:

• "mixture": Exact gamma-mixture method (default).

• "inverse": Inverse transform method.

• "rejection": Acceptance-rejection method using an exponential proposal.

Usage

rsujatha(
  n,
  theta,
  method = c("mixture", "inverse", "rejection"),
  lambda = NULL,
  M = NULL
)

Arguments

Value

A numeric vector of random observations.

Examples

set.seed(123)

## Gamma-mixture method (default)
x1 <- rsujatha(10, theta = 2)

## Inverse transform
x2 <- rsujatha(10, theta = 2, method = "inverse")

## Acceptance-Rejection
x3 <- rsujatha(10,
               theta = 2,
               method = "rejection",
               lambda = 1)

Variance-Covariance Matrix

Description

Variance-Covariance Matrix

Usage

## S3 method for class 'fitsujatha'
vcov(object, ...)

Arguments

Value

Variance-covariance matrix.