LDR: a package for likelihood-based sufficient dimension reduction.We introduce a software package running under Matlab that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operations, a graphical user interface is also provided for prototype computations.
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References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Chiancone, Alessandro; Forbes, Florence; Girard, Stéphane: Student sliced inverse regression (2017)
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- R. Cook; Zhihua Su; Yi Yang: envlp: A MATLAB Toolbox for Computing Envelope Estimators in Multivariate Analysis (2015) not zbMATH
- Kofi Adragni; Andrew Raim: ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction (2014) not zbMATH
- Lindsey, Charles D.; Sheather, Simon J.; Mckean, Joseph W.: Using sliced mean variance-covariance inverse regression for classification and dimension reduction (2014)
- Kofi Adragni; R. Cook; Seongho Wu: GrassmannOptim: An R Package for Grassmann Manifold Optimization (2012) not zbMATH
- Schott, James R.: A note on maximum likelihood estimation for covariance reducing models (2012)
- R. Cook; Liliana Forzani; Diego Tomassi: LDR: A Package for Likelihood-Based Sufficient Dimension Reduction (2011) not zbMATH
- Velilla, Santiago: On the structure of the quadratic subspace in discriminant analysis (2010)
- Cook, Dennis; Forzani, Liliana; Tomassi, Diego: LDR a package for likelihood-based sufficient dimension reduction (2009)