README.md
In haimbar/QREM: Quantile Regression via the EM algorithm
QREM - Quantile Regression EM algorithm
The estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights computed at the E-step as the expectation of independent generalized inverse-Gaussian variables.
The package extends QR to allow for random effects in the linear predictor. The generalized alternating minimization (GAM) framework also allows for variable selection in the QR, large P setting.
For more details on the theory, see the paper on ArXiV. For more usage documentation, see the QREM.pdf file in the doc/ subfolder in this repository .
haimbar/QREM documentation built on Aug. 27, 2022, 7:10 p.m.
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QREM - Quantile Regression EM algorithm
The estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights computed at the E-step as the expectation of independent generalized inverse-Gaussian variables. The package extends QR to allow for random effects in the linear predictor. The generalized alternating minimization (GAM) framework also allows for variable selection in the QR, large P setting.
For more details on the theory, see the paper on ArXiV. For more usage documentation, see the QREM.pdf file in the doc/ subfolder in this repository .
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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