Nothing

library(knitr) knitr::opts_chunk$set( fig.align = "center", fig.height = 5.5, fig.width = 6, warning = FALSE, collapse = TRUE, dev.args = list(pointsize = 10), out.width = "90%", par = TRUE ) knit_hooks$set(par = function(before, options, envir) { if (before && options$fig.show != "none") par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5) })

```
library(samurais)
```

**MRHLP**: Flexible and user-friendly probabilistic joint segmentation of
multivariate time series (or multivariate structured longitudinal data) with
smooth and/or abrupt regime changes by a mixture model-based multiple
regression approach with a hidden logistic process, fitted by the EM algorithm.

It was written in R Markdown, using the knitr package for production.

See `help(package="samurais")`

for further details and references provided by `citation("samurais")`

.

data("multivtoydataset")

K <- 5 # Number of regimes (mixture components) p <- 3 # Dimension of beta (order of the polynomial regressors) q <- 1 # Dimension of w (order of the logistic regression: to be set to 1 for segmentation) variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

n_tries <- 1 max_iter <- 1500 threshold <- 1e-6 verbose <- TRUE verbose_IRLS <- FALSE

mrhlp <- emMRHLP(multivtoydataset$x, multivtoydataset[,c("y1", "y2", "y3")], K, p, q, variance_type, n_tries, max_iter, threshold, verbose, verbose_IRLS)

mrhlp$summary()

mrhlp$plot(what = "regressors")

mrhlp$plot(what = "estimatedsignal")

mrhlp$plot(what = "loglikelihood")

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