Calculate the theoretical value of convertible bonds by given parameters, including B-S theory and Monte Carlo method.

Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but
    that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function
    in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the
    'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes"
    can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol()
    attempts to determine the most likely current Gene Symbol.

A tool to estimate IRT item parameters (2 PL) using CTT-based item statistics from small samples via artificial neural networks and regression trees.

Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.

Quantifies and assesses the significance of convergent evolution using multiple methods and measures as described in Stayton (2015) <DOI: 10.1111/evo.12729> and Grossnickle et al. 2023.  Also displays results in various ways.

Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    'survey' package.

General functions for convolutions of data. Moving average, running median, and other filters are available.
    Bibliography regarding the functions can be found in the following text.
    Richard G. Brereton (2003) <ISBN:9780471489771>.

Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.