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  "Title": "Variables Interpretability with Kernel PCA",
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  "Description": "The kernelized version of principal component analysis\n(KPCA) has proven to be a valid nonlinear alternative for\ntackling the nonlinearity of biological sample spaces. However,\nit poses new challenges in terms of the interpretability of the\noriginal variables. 'kpcaIG' aims to provide a tool to select\nthe most relevant variables based on the kernel PCA\nrepresentation of the data as in Briscik et al. (2023)\n<doi:10.1186/s12859-023-05404-y>. It also includes functions\nfor 2D and 3D visualization of the original variables (as\narrows) into the kernel principal components axes, highlighting\nthe contribution of the most important ones.",
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