Balance Check for Multiple Covariates in Matched Observational
Studies
Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

Balanced and spatially balanced sampling
This package includes methods that can select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.

Balanced and Spatially Balanced Sampling
Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.

Anton Grafström, Jonathan Lisic, Wilmer Prentius
Select balanced and spatially balanced probability samples in multi-dimensional spaces
with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of
the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012)
<doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012)
<doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and
the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013)
<doi:10.1002/env.2194>.

Statistical Inference and Sure Independence Screening via Ball
Statistics
XueQin Wang, WenLiang Pan, HePing Zhang, Hongtu Zhu, Yuan Tian, WeiNan Xiao, ChengFeng Liu, Jin Zhu
Hypothesis tests and sure independence screening (SIS) procedure based on ball
statistics, including ball divergence, ball covariance, and ball correlation,
are developed to analyze complex data. The ball divergence and ball covariance based distribution-free
tests are implemented to examine equality of multivariate distributions and independence
between random vectors of arbitrary dimensions. Furthermore, a generic non-parametric SIS
procedure based on ball correlation and all of its variants are implemented to tackle the
challenge in the context of ultra high dimensional data.