New distributions are still being suggested for better fitting of a distribution to data, as it is one of the most fundamental problems in terms of the parametric approach. One of such is weighted Lindley (WL) distribution (Ghitany et al. 2011). Even though WL distribution has become increasingly popular as a possible alternative to traditional distributions such as gamma and log normal distributions, fitting it to data has rarely been addressed in existing R packages. This is the reason we present the WLinfer package that implements overall statistical inference for WL distribution. In particular, WLinfer enables one to conduct the goodness of fit test, point estimation, bias correction, interval estimation, and the likelihood ratio test simply with the WL function which is at the core of this package. To assist users who are unfamiliar with WL distribution, we present a brief review followed by an illustrative example with R codes.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-042.zip
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For attribution, please cite this work as
Jang, et al., "The R Journal: WLinfer: Statistical Inference for Weighted Lindley Distribution", The R Journal, 2023
BibTeX citation
@article{RJ-2022-042,
author = {Jang, Yu-Hyeong and Kim, SungBum and Jung, Hyun-Ju and author), Hyoung-Moon Kim (Corresponding},
title = {The R Journal: WLinfer: Statistical Inference for Weighted Lindley Distribution},
journal = {The R Journal},
year = {2023},
note = {https://doi.org/10.32614/RJ-2022-042},
doi = {10.32614/RJ-2022-042},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {13-19}
}