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(flamingos)
```

**mixHMMR**: Simultaneous model-based clustering and segmentation of
heterogeneous and dynamical functional data (curves/times series) with changes
in regime by a mixture of gaussian regression models with hidden Markov
processes, fitted by the EM/Baum-Welch algorithm.

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

See `help(package="flamingos")`

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

.

data("toydataset") x <- toydataset$x Y <- t(toydataset[,2:ncol(toydataset)])

K <- 3 # Number of clusters R <- 3 # Number of regimes/states p <- 1 # Degree of the polynomial regression variance_type <- "heteroskedastic" # "heteroskedastic" or "homoskedastic" model

ordered_states <- TRUE n_tries <- 1 max_iter <- 1000 init_kmeans <- TRUE threshold <- 1e-6 verbose <- TRUE

mixhmmr <- emMixHMMR(X = x, Y = Y, K, R, p, variance_type, ordered_states, init_kmeans, n_tries, max_iter, threshold, verbose)

mixhmmr$summary()

mixhmmr$plot()

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