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().

Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.