vm: von Mises distribution

In LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

von Mises distribution

Description

Density, distribution function and random generation for the von Mises distribution.

Usage

dvm(x, mu = 0, kappa = 1, log = FALSE)

pvm(q, mu = 0, kappa = 1, from = NULL, tol = 1e-20)

rvm(n, mu = 0, kappa = 1, wrap = TRUE)

Arguments

x, q	vector of angles measured in radians at which to evaluate the density function.
mu	mean direction of the distribution measured in radians.
kappa	non-negative numeric value for the concentration parameter of the distribution.
log	logical; if TRUE, densities are returned on the log scale.
from	value from which the integration for CDF starts. If NULL, is set to mu - pi.
tol	the precision in evaluating the distribution function
n	number of observations. If length(n) > 1, the length is taken to be the number required.
wrap	logical; if TRUE, generated angles are wrapped to the interval [-pi, pi].

Details

This implementation of dvm allows for automatic differentiation with RTMB. rvm and pvm are simply wrappers of the corresponding functions from circular.

Value

dvm gives the density, pvm gives the distribution function, and rvm generates random deviates.

Examples

set.seed(1)
x = rvm(10, 0, 1)
d = dvm(x, 0, 1)
p = pvm(x, 0, 1)

LaMa documentation built on Nov. 5, 2025, 6:42 p.m.