Provides tools for preprocessing, feature extraction, and segmentation
    of three-dimensional forest point clouds derived from terrestrial laser scanning.
    Functions support creating height-above-ground (HAG) metrics, tiling, and sampling
    point clouds, generating training datasets, applying trained models to new point
    clouds, and producing per-point fuel classes such as stems, branches, foliage,
    and surface fuels. These tools support workflows for forest structure analysis,
    wildfire behavior modeling, and fuel complexity assessment. Deep learning 
    segmentation relies on the PointNeXt architecture described by Qian et al. 
    (2022) <doi:10.48550/arXiv.2206.04670>, while ground classification utilizes 
    the Cloth Simulation Filter algorithm by Zhang et al. (2016) <doi:10.3390/rs8060501>.

Fuel economy data from the EPA, 1985-2015,
    conveniently packaged for consumption by R users.

As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.

Provides core functions and utilities for packages and other code
    developed by Jordan Mark Barbone.

We facilitate the analysis of full factorial mating designs with mixed-effects models. The package contains six vignettes containing detailed examples.

Creates a full rank matrix out of a given matrix.
  The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, 'fullRankMatrix' can be applied to any matrix to make it full rank.
  It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications.
  This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) <doi:10.18637/jss.v028.i05>.