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gbmodes - Analyze multimodality in univariate data

Author

       Written by Giulio Bottazzi

Description

       Multimodality  analysis  via  kernel  density estimation. Compute the maximal kernel bandwidth h_c(M) for
       which #(modes in the estimated density)>M.The significance of h_c is  computed  via  smoothed  bootstrap.
       Read data from std.  input. Print the couple h_c p(h_c) and the modal values (set with -O).

Name

       gbmodes - Analyze multimodality in univariate data

Options

-n     number of equispaced points where the density is computed (default 100)

       -m     number of modes (default 1)

       -r     search range for modes; comma separated couple 'data_min,data_max'

       -s     scale the search range to [min*s+(1-s)*Max,Max*s+(1-s)*min] (default 1)

       -e     relative tolerance on h_c value (default 1e-6)

       -S     significance level (for Hall-York correction) (default .05)

       -t     number of bootstrap trials used for significance computation (default 1000)

       -R     RNG seed for bootstrap trials (default 0)

       -K     choose the kernel to use: 0 Gaussian or 1 Laplacian  (default 0)

       -v     verbose mode (more verbose if provided 2 times)

       -O     output type (default 0)

       0      h_c p(h_c) [modes locations]

       1      h_c [modes locations and heights]

       -F     specify the input fields separators (default " \t")

       -h     this help

Reporting Bugs

Synopsis

gbmodes [options]

See Also