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.

Functions for computing a standardized moderation effect
             in moderated regression and forming its confidence interval
             by nonparametric bootstrapping as proposed in
             Cheung, Cheung, Lau, Hui, and Vong (2022)
             <doi:10.1037/hea0001188>. Also includes simple-to-use
             functions for computing conditional effects (unstandardized
             or standardized) and plotting moderation effects.

Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.

Contains more modern tools for causal inference using regression 
      standardization. Four general classes of models are implemented; generalized 
      linear models, conditional generalized estimating equation models, 
      Cox proportional hazards models, and shared frailty gamma-Weibull models. 
      Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. 
      Also includes functionality for doubly robust estimation for generalized linear models 
      in some special cases, and the ability to implement custom models.