Fast k-nearest neighbors (K-NN) and principal component
    analysis (PCA) imputation algorithms for missing values in
    high-dimensional numeric matrices, i.e., epigenetic data. For
    extremely high-dimensional data with ordered features, a sliding
    window approach for K-NN or PCA imputation is provided.  Additional
    features include group-wise imputation (e.g., by chromosome),
    hyperparameter tuning with repeated cross-validation, multi-core
    parallelization, and optional subset imputation. The K-NN algorithm is
    described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M.,
    Brown, P. and Botstein, D.  (1999) "Imputing Missing Data for Gene
    Expression Arrays". The PCA imputation is an optimized version of the
    imputePCA() function from the 'missMDA' package described in: Josse,
    J. and Husson, F.  (2016) <doi:10.18637/jss.v070.i01> "missMDA: A
    Package for Handling Missing Values in Multivariate Data Analysis".

Provides type-stable rolling window functions over any R data
    type. Cumulative and expanding windows are also supported. For more
    advanced usage, an index can be used as a secondary vector that
    defines how sliding windows are to be created.

Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.

A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.

Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.