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>.

Performs fast variable selection in high-dimensional settings while controlling the 
    false discovery rate (FDR) at a user-defined target level. The package is based on the paper 
    Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.

Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) <doi:10.7717/peerj.17036>.