Provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2>
    methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e.,
    non-negative real numbers), and balances (i.e., any real number). The framework can
    accommodate any number of units, any number of coders, and missingness; and
    can be used to measure agreement with a gold standard, intra-coder agreement,
    and/or inter-coder agreement. Frequentist inference is supported for all levels
    of measurement. Bayesian inference is supported for continuous scores only.

Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.

It's a Super K-Nearest Neighbor(SKNN) classification method with using kernel density to describe weight of the distance between a training observation and the testing sample. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN.

Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.

A tool to calculate sky illuminance values (in lux) for both sun and moon. The
 model is a translation of the Fortran code by Janiczek and DeYoung (1987)
 <https://archive.org/details/DTIC_ADA182110>.

Calculate geolocations by light using template matching. 
 The routine uses a calibration free optimization of a sky illuminance model to 
 determine locations robustly using a template matching approach,
 as described by Ekstrom (2004) <https://nipr.repo.nii.ac.jp/records/2496>,
 and behaviourly informed constraints (step-selection).