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.

This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.