acepack

Two nonparametric methods for multiple regression transform selection are provided. The first, Alternating Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <[doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low see Tibshirani, R. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <[doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics. A permutation independence test is included from Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure linked to maximum correlation". Scandinavian Journal of Statistics. 52(1):145-169 <[doi:10.1111/sjos.12733>].

Last 30 days

This package has been downloaded 6,580 times in the last 30 days. A solid achievement! Enough downloads to get noticed at department meetings. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 232 times.

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Last 365 days

This package has been downloaded 68,657 times in the last 365 days. The kind of number that gets mentioned in a keynote speech. Well done! The day with the most downloads was Jul 12, 2024 with 1,161 downloads.

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