Description Usage Arguments Value Note References See Also Examples
Functions to Estimate the Conway Maxwell Poisson Discrete Probability Distribution via maximum likelihood.
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x |
A vector of counts (one per observation). |
cutoff |
Calculate estimates conditional on exceeding this value. |
cutabove |
Calculate estimates conditional on not exceeding this value. |
guess |
Initial estimate at the MLE. |
method |
Method of optimization. See "optim" for details. |
conc |
Calculate the concentration index of the distribution? |
hellinger |
Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood. |
hessian |
Calculate the hessian of the information matrix (for use with calculating the standard errors. |
theta |
vector of MLE of the parameters. |
asycov |
asymptotic covariance matrix. |
asycor |
asymptotic correlation matrix. |
se |
vector of standard errors for the MLE. |
conc |
The value of the concentration index (if calculated). |
See the working papers on http://www.csss.washington.edu/Papers for details.
Based on the C code in the package compoisson written by Jeffrey Dunn (2008).
compoisson: Conway-Maxwell-Poisson Distribution, Jeffrey Dunn, 2008, R package version 0.3
ayulemle, awarmle, simcmp
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