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Showing results 1 to 10 of 1,364.


Function smuce [FDRSeg v1.0-2]
keywords
nonparametric
title
Piecewise constant regression with SMUCE
description
Compute the SMUCE estimator for one-dimensional data with i.i.d. Gaussian noises.
Function teethfun [FDRSeg v1.0-2]
keywords
nonparametric
title
Teeth function
description
Creat the teeth function with specified lengths and number of change-points.
Function v_measure [FDRSeg v1.0-2]
keywords
nonparametric
title
Compute V-measure
description
Compute V-measure, a segmentation evaluation measure, which is based upon two criteria for clustering usefulness, homogeneity and completeness.
Function fdrseg [FDRSeg v1.0-2]
keywords
nonparametric
title
Piecewise constant regression with FDRSeg
description
Compute the FDRSeg estimator for one-dimensional data with i.i.d. Gaussian noises.
Function simulQuantile [FDRSeg v1.0-2]
keywords
nonparametric
title
Quantile simulations
description
Simulate the quantiles of multiscale statistics for SMUCE, FDRSeg, and D-FDRSeg under null hypothesis.
Function computeFdp [FDRSeg v1.0-2]
keywords
nonparametric
title
Compute false discovery proportion (FDP)
description
Compute false discovery proportion for estimated change-points, see (Li et al., 2015) for a detailed explanation.
Function dfdrseg [FDRSeg v1.0-2]
keywords
nonparametric
title
Piecewise constant regression with D-FDRSeg
description
Compute the D-FDRSeg estimator for one-dimensional data with dependent Gaussian noises, especially for ion channel recordings, see (Hotz et al., 2013; Li et al., 2015) for further details.
Function evalStepFun [FDRSeg v1.0-2]
keywords
nonparametric
title
Evaluate step function
description
Transform the return value by smuce, fdrseg, or dfdrseg into a numeric vector.
Function test_features [biogram v1.4]
keywords
nonparametric
title
Permutation test for feature selection
description
Performs a feature selection on positioned n-gram data using a Fisher's permutation test.
Function opt.bw [MKLE v0.05]
keywords
nonparametric
title
Optimal bandwidth for the maximum kernel likelihood estimator
description
Estimates the optimal bandwidth for the maximum kernel likelihood estimator using a Gaussian kernel for a given dataset using the bootstrap.