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  "Description": "Two nonparametric methods for multiple regression\ntransform selection are provided. The first, Alternating\nConditional Expectations (ACE), is an algorithm to find the\nfixed point of maximal correlation, i.e. it finds a set of\ntransformed response variables that maximizes R^2 using\nsmoothing functions [see Breiman, L., and J.H. Friedman. 1985.\n\"Estimating Optimal Transformations for Multiple Regression and\nCorrelation\". Journal of the American Statistical Association.\n80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also\nincluded is the Additivity Variance Stabilization (AVAS) method\nwhich works better than ACE when correlation is low [see\nTibshirani, R. 1986. \"Estimating Transformations for Regression\nvia Additivity and Variance Stabilization\". Journal of the\nAmerican Statistical Association. 83:394-405.\n<doi:10.1080/01621459.1988.10478610>]. A good introduction to\nthese two methods is in chapter 16 of Frank Harrell's\n\"Regression Modeling Strategies\" in the Springer Series in\nStatistics. A permutation independence test is included from\n[Holzmann, H., Klar, B. 2025. \"Lancaster correlation - a new\ndependence measure linked to maximum correlation\". Scandinavian\nJournal of Statistics. 52(1):145-169 <doi:10.1111/sjos.12733>].",
  "Title": "ACE and AVAS for Selecting Multiple Regression Transformations",
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