Provides a set of exploratory data analysis (EDA) tools for 
    visualizing trends, diagnosing data types for beginner-friendly workflows, 
    and automatically routing to suitable statistical tests or trend exploration 
    models. Includes unified plotting functions for trend lines, grouped boxplots, 
    and comparative scatterplots; automated statistical testing (e.g., t-test, 
    Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size 
    calculation; and model-based trend analysis using generalized additive 
    models (GAM) for count data, generalized linear models (GLM) for continuous 
    data, and zero-inflated models (ZIP/ZINB) for count data with potential 
    zero-inflation. 
    Also supports time-window continuity checks, cross-year 
    handling in compare_monthly_cases(), and ARIMA-ready preparation with 
    stationarity diagnostics, ensuring consistent parameter styles for 
    reproducible research and user-friendly workflows.Methods are 
    based on R Core Team (2024) <https://www.R-project.org/>, 
    Wood, S.N.(2017, ISBN:978-1498728331),
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, 
    Simon Jackman (2024) <https://github.com/atahk/pscl/>,    
    Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.

Matrix factorization for multivariate time series with both low rank and temporal structures. 
    The procedure is the one proposed by Alquier, P. and Marie, N. 
    "Matrix factorization for multivariate time series analysis." 
    Electronic Journal of Statistics, 13(2), 4346-4366 (2019).

Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) <doi:10.1080/00949655.2018.1482492>.

Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package.
    Use four spaces when indenting paragraphs within the Description.

Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.