A collection of functions to construct sets of orthogonal
        polynomials and their recurrence relations. Additional
        functions are provided to calculate the derivative, integral,
        value and roots of lists of polynomial objects.

Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.

A first implementation of automated parsing of user stories, when used 
  to defined functional requirements for operational research mathematical 
  models. It allows reading user stories, splitting them on the who-what-why template,
  and classifying them according to the parts of the mathematical model that 
  they represent. Also provides semantic grouping of stories, 
  for project management purposes.

Microarray probe ID is not convenient for further enrichment analysis and target gene selection.
    The package is created for the rice microarray probe ID conversion.
    This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research.
    For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes.
    Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.

Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).

Optimal Subset Cardinality Regression (OSCAR) models offer
    regularized linear regression using the L0-pseudonorm, conventionally
    known as the number of non-zero coefficients. The package estimates an
    optimal subset of features using the L0-penalization via
    cross-validation, bootstrapping and visual diagnostics. Effective
    Fortran implementations are offered along the package for finding
    optima for the DC-decomposition, which is used for transforming the
    discrete L0-regularized optimization problem into a continuous
    non-convex optimization task. These optimization modules include DBDC
    ('Double Bundle method for nonsmooth DC optimization' as described in
    Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
    Memory Bundle Method for large-scale nonsmooth optimization' as
    in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The
    OSCAR models are comprehensively exemplified in Halkola et al. (2023) 
    <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families
    are supported: Cox, logistic, and Gaussian.