A bioinformatics pipeline for performing taxonomic
    assignment of DNA metabarcoding sequence data while considering
    geographic location. A detailed tutorial is available at
    <https://urodelan.github.io/Local_Taxa_Tool_Tutorial/>.
    A manuscript describing these methods is in preparation.

Computation of local false discovery rates.

Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.

Provides test of second-order stationarity for time
	series (for dyadic and arbitrary-n length data). Provides
	localized autocovariance, with confidence intervals,
	for locally stationary (nonstationary) time series.
	See Nason, G P (2013) "A test for second-order stationarity and
	approximate confidence intervals for localized autocovariance
	for locally stationary time series." Journal of the Royal Statistical
	Society, Series B, 75, 879-904.  <doi:10.1111/rssb.12015>.

Datasets for the fourth edition of "Statistics: Unlocking the Power of Data" by Lock^5
    Includes versions of datasets from earlier editions.

Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data by kernel-weighted estimating equation.

Computes local polynomial estimators for 
  the regression and also density. It comprises several 
  different utilities to handle kernel estimators.

Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.