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get the lambda value that yield certain number of non-zero coefficients
● Data Source:
BioConductor
● Keywords:
● Alias: getLambdaNcoef
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Lasso penalized linear regression with different optimizers
● Data Source:
BioConductor
● Keywords:
● Alias: lasso
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Cross validation lasso. This function optimizes the lasso solution for correlated regulators by an algorithm. this algorithm chooses the minimum lambda since the penalized package by default use 0 for the minimum, which sometimes take a long time to compute
● Data Source:
BioConductor
● Keywords:
● Alias: lasso.cv
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Multi-split lasso as described in Meinshausen 2009
● Data Source:
BioConductor
● Keywords:
● Alias: lasso.multiSplit
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The function performs lasso with multiple random sample splits, selecting coefficients that are simultaneously non-zero in both subsets of samples.
● Data Source:
BioConductor
● Keywords:
● Alias: lasso.simultaneous
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point-wise controled lasso stability selection
● Data Source:
BioConductor
● Keywords:
● Alias: lasso.stability
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Refit the regressions given matrices of responses, predictors, and the coefficients/interactions matrix. This is typically used after the lasso, since the coefficients were shrinked.
● Data Source:
BioConductor
● Keywords:
● Alias: lmMatrixFit
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Various optimization methods for Lasso inference with matrix wrapper.
● Data Source:
BioConductor
● Keywords: package
● Alias: lol, lol-package
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This function wraps up different types of lasso optimizers and perform multiple, independent lasso inference on matrix responses. If the dimensionality of the input is small, the function converts the matrix of input response into a vector and solves the problem with one lasso inference. Otherwise, lasso regression is performed independently for each variables in the response matrix.
● Data Source:
BioConductor
● Keywords:
● Alias: matrixLasso
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Plot different measurements across the genome such as copy number amplifications and deletions.
● Data Source:
BioConductor
● Keywords:
● Alias: plotGW
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providing 
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