coord2K: A coordinate descent algorithm to find the maximum likelihood...
In pzwiernik/mtp2: Working with Gaussian totally positive distributions
Description
Usage
Arguments
Value
Examples
Description
This function implements a simple coordinate descent algorithm to find the maximum likelihood estimator over
Gaussian MTP2 distributions. For details see Lauritzen, Uhler, Zwiernik (2017).
Usage
1
Arguments
S
the sample covariance matrix
n
the sample size (default 1), relevant for testing
K0
the starting point (default K0=solve(diag(diag(S))))
tol
the convergence tolerance (default tol=1e-8)
Value
the optimal value of the concentration matrix
the number of iterations the algorithm needed to converge
the corresponding value of the log-likelihood
Examples
1
pzwiernik/mtp2 documentation built on Aug. 9, 2020, 12:34 p.m.
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Description Usage Arguments Value Examples
Description
This function implements a simple coordinate descent algorithm to find the maximum likelihood estimator over Gaussian MTP2 distributions. For details see Lauritzen, Uhler, Zwiernik (2017).
Usage
1 |
Arguments
S |
the sample covariance matrix |
n |
the sample size (default 1), relevant for testing |
K0 |
the starting point (default K0=solve(diag(diag(S)))) |
tol |
the convergence tolerance (default tol=1e-8) |
Value
the optimal value of the concentration matrix
the number of iterations the algorithm needed to converge
the corresponding value of the log-likelihood
Examples
1 |
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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