Grey model is commonly used in time series forecasting when statistical assumptions are violated with a limited number of data points. The minimum number of data points
             required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns
             the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) <DOI:10.1016/j.energy.2006.11.014>,
             Hsu, L. and Wang, C. (2007).<DOI:10.1016/j.techfore.2006.02.005>.

Model infectious disease dynamics in populations 
    with multiple subgroups having different vaccination rates, transmission
    characteristics, and contact patterns. Calculate final and intermediate
    outbreak sizes, form age-structured contact models with automatic fetching
    of U.S. census data, and explore vaccination scenarios with an interactive
    'shiny' dashboard for a model with two subgroups, as described in Nguyen et
    al. (2024) <doi:10.1016/j.jval.2024.03.039> and Duong et al. (2026)
    <doi:10.1093/ofid/ofaf695.217>.

Two method new of multigroup and simulation of data.
  The first technique called multigroup PCA (mgPCA) this multivariate exploration approach
  that has the idea of considering the structure of groups and / or different types
  of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality
  Reduction (MDR) it is another multivariate exploration method that is based on
  projections. In addition, a method called Single Dimension Exploration (SDE) was
  incorporated for to analyze the exploration of the data. It could help us in a better
  way to observe the behavior of the multigroup data with certain variables of interest.

It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).