fitODBOD

In Twitter I recently observed people tweeting two photos,where under one the caption is “How it started” and the other “How it ended”. I figured why not change this humorous trend to journal articles as well. We start with an initial submission but after rejections and comments the article might get severely scrutinized. Not always but sometimes this leads to somewhat of a stunning transformation of the research idea. Hence, below I have provided how my initial article for the “fitODBOD” R package has evolved.

The R package fitODBOD can be used to identify the best-fitting model for Over-dispersed Binomial Outcome Data(BOD).The Triangular Binomial(TriBin),Beta-Binomial(BetaBin), Kumaraswamy Binomial (KumBin), Gaussian Hypergeometric Generalized …

Binomial Outcome Data has vast amount use in the field of biology, medicine and epidemiology. Modelling such data relative to Binomial Mixture Distributions were discussed because Binomial distribution fails to model the data. The reason is actual …

Using [pkgdown](https://pkgdown.r-lib.org/) a genuine website for fitODBOD was generated. There is no extra work to be done here, because pkgdown uses our existing man files and vignettes to create this website. It is quite easy and convenient for a …

Brief introduction on Binomial Distribution and its expansion based on data.

Developing your own package in R with steps and helpful materials.

Using different analytical methods from the mle and mle2 optimizing function.

Comparing the analytical methods of the maxLik optimizing function.

Opimizing using 'optim' function and comparing analytical methods.

Comparing optimization functions for the estimation of shape parameters from the Beta-Binomial distribution.