Methods for choosing the rank of an SVD (singular value decomposition) 
    approximation via cross validation. The package provides both Gabriel-style 
    "block" holdouts and Wold-style "speckled" holdouts. It also includes an 
    implementation of the SVDImpute algorithm. For more information about
    Bi-cross-validation, see Owen & Perry's 2009 AoAS article
    (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).

Algorithms developed for binned data analysis,
  gene expression data analysis and
  measurement error models for ordinal data analysis.

The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.

It brings together several aspects of biodiversity
    data-cleaning in one place. 'bdc' is organized in thematic modules
    related to different biodiversity dimensions, including 1) Merge
    datasets: standardization and integration of different datasets; 2)
    Pre-filter: flagging and removal of invalid or non-interpretable
    information, followed by data amendments; 3) Taxonomy: cleaning,
    parsing, and harmonization of scientific names from several taxonomic
    groups against taxonomic databases locally stored through the
    application of exact and partial matching algorithms; 4) Space:
    flagging of erroneous, suspect, and low-precision geographic
    coordinates; and 5) Time: flagging and, whenever possible, correction
    of inconsistent collection date. In addition, it contains
    features to visualize, document, and report data quality – which is
    essential for making data quality assessment transparent and
    reproducible. The reference for the methodology is Bruno et al. (2022)
    <doi:10.1111/2041-210X.13868>.

The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.

A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.