Implementations of two empirical versions the kernel partial correlation (KPC) coefficient
and the associated variable selection algorithms. KPC is a measure of the strength of conditional
association between Y and Z given X, with X, Y, Z being random variables taking values in
general topological spaces. As the name suggests, KPC is defined in terms of kernels on
reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number
between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if
and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on
geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is
consistent under very weak conditions. The other empirical estimator, defined using conditional
mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable
conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph
based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm
based on the RKHS based estimator. For more details on KPC, its empirical estimators and its
application on variable selection, see Huang, Z., N. Deb, and B. Sen (2020). “Kernel partial
correlation coefficient – a measure of conditional dependence”