In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. For method details see, Bhowmik, A., Varghese, E., Jaggi, S. and Varghese, C. (2017) <doi:10.1080/03610926.2016.1152490>. This package generates all possible minimally changed two-level half-fractional factorial designs for different experimental setups along with various statistical criteria to measure the performance of these designs through a user-friendly interface. It consist of the function minimal.2halfFFD() which launches the application interface.

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>.