| Type: | Package |
| Title: | The Scaled von Mises-Fisher Distribution |
| Version: | 1.0 |
| Date: | 2026-06-11 |
| Author: | Michail Tsagris [aut, cre] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | Rfast, stats |
| Description: | Functions to perform maximum likelihood estimation of and random value simulation from the scaled von Mises-Fisher distribution. The distribution is elliptical symmetric and can be applied to spherical and hyper-spherical data. The reference paper is Scealy J.L. and Wood A.T.A. (2019), <doi:10.1080/01621459.2019.1585249>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2026-06-11 13:06:51 UTC; mtsag |
| Repository: | CRAN |
| Date/Publication: | 2026-06-18 14:20:02 UTC |
The Scaled von Mises–Fisher Distribution
Description
Functions to perform maximum likelihood estimation of and random value simulation from the scaled von Mises–Fisher distribution. For more information see Scealy and Wood (2019).
Details
| Package: | svmf |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2026-06-11 |
Maintainers
Michail Tsagris <mtsagris@uoc.gr>.
Author(s)
Michail Tsagris mtsagris@uoc.gr
References
Scealy J.L. and Wood A.T.A. (2019). Scaled von Mises-Fisher distributions and regression models for paleomagnetic directional data. Journal of the American Statistical Association, 114(528): 1547–1560.
Random value simulation from the scaled von Mises–Fisher distribution
Description
Random value simulation from the scaled von Mises–Fisher distribution.
Usage
rsvmf(n, mu, a, kappa, Gamma = NULL)
Arguments
n |
The sample size. |
mu |
The mean direction. |
a |
The vector of alphas. |
kappa |
The concentration parameter. |
Gamma |
The matrix with the |
Value
A matrix with the simulated data.
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Scealy J.L. and Wood A.T.A. (2019). Scaled von Mises-Fisher distributions and regression models for paleomagnetic directional data. Journal of the American Statistical Association, 114(528): 1547–1560.
See Also
Examples
mu <- rnorm(3)
mu <- mu / sqrt( sum(mu^2) )
a <- c(1, 3, 1/3)
y <- rsvmf(1000, mu, a, 20)
svmf.mle(y)
Maximum likelihood estimation of the scaled von Mises–Fisher distribution
Description
Maximum likelihood estimation of the scaled von Mises–Fisher distribution.
Usage
svmf.mle(y, a1 = 1, maxit_outer = 100, maxit_V = 500, tol = 1e-6)
Arguments
y |
A numerical matrix with the (hyper-)spherical observations. |
a1 |
The value of the |
maxit_outer |
The maximum number of iterations to perform. |
maxit_V |
The maximum number of iterations to perform to estimate the |
tol |
The tolerance value until convergence of the log-likelihood. |
Value
A list including:
mu |
The estimated mean direction. |
kappa |
The estimated concentration parameter. |
a |
The estimated vector of alphas, the first value is 1, by default. |
loglik |
the log-likelihood value. |
Gamma |
The estimated matrix with the |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Scealy J.L. and Wood A.T.A. (2019). Scaled von Mises-Fisher distributions and regression models for paleomagnetic directional data. Journal of the American Statistical Association, 114(528): 1547–1560.
See Also
Examples
mu <- rnorm(3)
mu <- mu / sqrt( sum(mu^2) )
a <- c(1, 3, 1/3)
y <- rsvmf(1000, mu, a, 20)
svmf.mle(y)