Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

The identification of sea regimes from environmental multivariate times series is complicated
by the mixed linear–circular support of the data, by the occurrence of missing values, by the …

The derivation of loss distribution from insurance data is a very interesting research topic but
at the same time not an easy task. To find an analytic solution to the loss distribution may be …

A new hidden Markov model is proposed for the analysis of cylindrical time series, that is,
bivariate time series of intensities and angles. It allows us to segment cylindrical time series …

Round genomes are found in bacteria, plant chloroplasts, and mitochondria. Genetic or
epigenetic marks can present biologically interesting clusters along a circular genome. The …

We introduce a multivariate hidden Markov model to jointly cluster time‐series observations
with different support, that is, circular and linear. Relying on the general projected normal …

This article is considered to be the first to deal with the problem of outlier‐detection in
multivariate circular data. The proposed algorithm is an extension of the Local Outlier Factor …

A regression model for correlated circular data is proposed by assuming that samples of
angular measurements are drawn from a multivariate von Mises distribution with mean and …

The analysis of circular data has been recently the focus of a wide range of literature, with
the general objective of providing reliable parameter estimates in the presence of …

CircSpaceTime is the only R package, currently available, that implements Bayesian models
for spatial and spatio-temporal interpolation of circular data. Such data are often found in …