Fast k-nearest neighbors (K-NN) and principal component
    analysis (PCA) imputation algorithms for missing values in epigenetic
    data or other high-dimensional numeric matrices. For PCA, a locally
    optimal block preconditioned conjugate gradient (LOBPCG) eigensolver
    with warm starts of both the eigenblock and search direction is also
    supported. Two complementary imputation strategies are available.
    Group-wise imputation (e.g., by chromosome) is recommended for
    Illumina DNA methylation microarrays (e.g., 450K, EPIC) and other
    matrices with groupable columns. A sliding window approach for K-NN or
    PCA imputation is recommended only for whole-genome methylation data
    such as whole-genome bisulfite sequencing (WGBS) or Enzymatic
    Methyl-seq (EM-seq). The package also supports hyperparameter tuning
    via repeated cross-validation. 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 reimplementation 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.