Fitting models for, and simulation of, trend locally stationary 
    wavelet (TLSW) time series models, which take account of time-varying 
    trend and dependence structure in a univariate time series. The TLSW model, 
    and its estimation, is described in McGonigle, Killick and Nunes (2022a) 
    <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will 
    likely want to start with the TLSW function.

Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.

Analysis of annual average ocean water level time series, providing
    improved estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on singular spectrum analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also includes a range of diagnostic tools to inspect
    the components comprising the original time series which enables expert
    interpretation and selection of likely trend components. A wide range of
    screen and plot to file options are available in the package.

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.