Count data is prevalent and informative, with widespread
    application in many fields such as social psychology, personality, and
    public health. Classical statistical methods for the analysis of count
    outcomes are commonly variants of the log-linear model, including
    Poisson regression and Negative Binomial regression. However, a
    typical problem with count data modeling is inflation, in the sense
    that the counts are evidently accumulated on some integers. Such an
    inflation problem could distort the distribution of the observed
    counts, further bias estimation and increase error, making the classic
    methods infeasible. Traditional inflated value selection methods based
    on histogram inspection are easy to neglect true points and
    computationally expensive in addition. Therefore, we propose a
    multiple-inflated negative binomial model to handle count data
    modeling with multiple inflated values, achieving data-driven inflated
    value selection. The proposed approach provides simultaneous
    identification of important regression predictors on the target count
    response as well. More details about the proposed method are described in 
    Li, Y., Wu, M., Wu, M., & Ma, S. (2023) <arXiv:2309.15585>.

Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.

66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().

Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or 'Freemind' codes or 'Mermaid' mind map codes, and vice versa. 'Freemind' mind map ('.mm') files can be opened by or imported to common mind map software such as 'Freemind' (<https://freemind.sourceforge.io/wiki/index.php/Main_Page>). 'Mermaid' mind map codes (<https://mermaid.js.org/>) can be directly embedded in documents.