| Type: | Package |
| Title: | Fit Modified Weibull-Type Distributions |
| Version: | 0.1.0 |
| Description: | Provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | stats |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-10-08 06:28:36 UTC; Reneuit |
| Author: | Dr Ajaykumar Rangoli [aut, cre] |
| Maintainer: | Dr Ajaykumar Rangoli <drajaykumarmr1008@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-10 20:10:02 UTC |
Fit Modified Weibull-Type Distributions
Description
Fits eight modified Weibull-type distributions using maximum likelihood estimation. Returns estimates, log-likelihood, AIC, and BIC.
Usage
WeibullModiAMR(x, dist = "Alwasel2009")
Arguments
x |
Numeric vector of positive data. |
dist |
Character; choose one of:
|
Value
A list containing:
estimates |
Estimated parameters. |
loglik |
Maximized log-likelihood. |
AIC |
Akaike Information Criterion. |
BIC |
Bayesian Information Criterion. |
Distributions
The following modified Weibull-type distributions are included in the package, along with their respective probability density functions (PDFs) and references:
-
Rangoli2023:
f(x) = (a b x^{b-1} + \frac{g}{x^2}) \exp(a x^b - \frac{g}{x}) \exp(-\exp(a x^b - \frac{g}{x}))Reference: Rangoli, A. M. (2024). doi:10.22271/maths.2024.v9.i4b.1771
-
Peng2014:
f(x) = \alpha \exp(-\lambda/x) (\beta x^{\beta-1} + \lambda x^{\beta-2}) \exp(-\alpha x^{\beta} \exp(-\lambda/x))Reference: Peng, X. (2014). doi:10.1016/j.ress.2013.07.007
-
Lai2003:
f(x) = a (b + l x) x^{b-1} \exp(l x) \exp(-a x^{b} \exp(l x))Reference: Lai, C. D. (2003). doi:10.1109/TR.2002.805788
-
Xie1996:
f(x) = (a b x^{b-1} + g l x^{l-1}) \exp(-(a x^{b} + g x^{l}))Reference: Xie, M. (1996). doi:10.1016/0951-8320(95)00149-2
-
Sarhan2009:
f(x) = (a b x^{b-1} + g) \exp(-(a x^{b} + g x))Reference: Sarhan, A. M. (2009). https://www.researchgate.net/publication/228695122_Modified_Weibull_distribution
-
Rangoli2025:
f(x) = (g l x^{l-1} + a b x^{b-1} \exp(x^{b}) / b^{a}) \exp(-((a/b^{a})(\exp(x^{b}) - 1) + g x^{l}))Reference: Rangoli, A. M. (2025). doi:10.7759/cureus.77347
-
Mustafa2012:
f(x) = (\alpha \beta x^{\beta-1} + \lambda \exp(-\lambda x)) \exp(-(\alpha x^{\beta} + \lambda x))Reference: Mustafa, A. (2012). doi:10.1016/j.ress.2013.07.007
-
Alwasel2009:
f(x) = (a + b g x^{g-1}) \exp(-(a x + b x^{g}))Reference: Alwasel, I. (2009). https://www.researchgate.net/publication/228817485_Statistical_Inference_of_a_Competing_Risks_Model_with_Modified_Weibull_Distributions
Examples
# Load your package
library(WeibullModiAMR)
# Example: Fit Rangoli2023 distribution
x <- rexp(50, rate = 1)
res <- WeibullModiAMR(x, dist = "Rangoli2023")
print(res)