| Port variant | std |
| Summary | ACE and AVAS multiple regression transformations |
| Package version | 1.6.3 |
| Homepage | No known homepage |
| Keywords | cran |
| Maintainer | CRAN Automaton |
| License | Not yet specified |
| Other variants | There are no other variants. |
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| Last modified | 14 SEP 2025, 21:31:39 UTC |
| Port created | 11 FEB 2021, 15:40:28 UTC |
| single | acepack: ACE and AVAS for Selecting Multiple Regression Transformations 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>]. |
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| main | mirror://CRAN/src/contrib https://loki.dragonflybsd.org/cranfiles/ |
| R-nlts:std | Nonlinear Time Series Analysis |