rigff

Estimation of the parameters of a
 reciprocal inverse Gaussian distribution.

models

regression

An implementation of about 6 major classes of
statistical regression models. The central algorithm is
Fisher scoring and iterative reweighted least squares.
At the heart of this package are the vector generalized linear
and additive model (VGLM/VGAM) classes. VGLMs can be loosely
thought of as multivariate GLMs. VGAMs are data-driven
VGLMs that use smoothing. The book "Vector Generalized
Linear and Additive Models: With an Implementation in R"
(Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
the statistical framework and the package. Currently only
fixed-effects models are implemented. Many (100+) models and
distributions are estimated by maximum likelihood estimation
(MLE) or penalized MLE. The other classes are RR-VGLMs
(reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
RCIMs (row-column interaction models)---these classes perform
constrained and unconstrained quadratic ordination (CQO/UQO)
models in ecology, as well as constrained additive ordination
(CAO). Hauck-Donner effect detection is implemented.
Note that these functions are subject to change;
see the NEWS and ChangeLog files for latest changes.

Thomas Yee

VGAM

Vector Generalized Linear and Additive Models

Cleve Moler

rigff function

<dl> <dt>lmu, llambda</dt>
<dd>Link functions for <code>mu</code> and <code>lambda</code>.
 See <code>Links</code> for more choices.</dd> <dt>imu, ilambda</dt>
<dd>Initial values for <code>mu</code> and <code>lambda</code>.
 A <code>NULL</code> means a value is computed internally.</dd></dl>

Arguments

Author

Reciprocal Inverse Gaussian distribution — rigff

<dl>

 <dt>lmu, llambda</dt>
<dd>Link functions for <code>mu</code> and <code>lambda</code>.
 See <code>Links</code> for more choices.

</dd>

 <dt>imu, ilambda</dt>
<dd>Initial values for <code>mu</code> and <code>lambda</code>.
 A <code>NULL</code> means a value is computed internally.

</dd>

</dl>

rigff: Reciprocal Inverse Gaussian distribution

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples