Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.

Algorithms of nonparametric sequential test and online 
  change-point detection for streams of univariate (sub-)Gaussian, binary, 
  and bounded random variables, introduced in following publications -
  Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>,
  Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.

Provides crop yield and meteorological data for Ontario, Canada. 
  Includes functions for fitting and predicting data using spatio-temporal models, as well as
  tools for visualizing the results. The package builds upon existing R packages, including
  'copula' (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and
  'bsts' (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.

Implements the ST-DBSCAN (spatio-temporal density-based spatial
    clustering of applications with noise) clustering algorithm for detecting
    spatially and temporally dense regions in point data, with a fast C++ backend
    via 'Rcpp'. Birant and Kut (2007) <doi:10.1016/j.datak.2006.01.013>.

Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.

Provides functions to compute standardized differences for numeric, binary,
    and categorical variables on Apache Spark DataFrames using 'sparklyr'.
    The implementation mirrors the methods used in the 'stddiff' package but
    operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) 
    <doi:10.32614/CRAN.package.stddiff> for reference.

Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.