# MEDseq R Package

## Mixtures of
Exponential-Distance Models

## for
Clustering Longitudinal Life-Course Sequences

## with Gating
Covariates and Sampling Weights

### Written by Keefe Murphy

## Description

Fits *MEDseq* models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>,
i.e. fits mixtures of exponential-distance models for clustering
longitudinal/categorical life-course sequence data via the EM/CEM
algorithm. A family of parsimonious precision parameter constraints are
accommodated. So too are sampling weights. Gating covariates can be
supplied via formula interfaces. Visualisation of the results of such
models is also facilitated.

The most important function in the **MEDseq** package
is: `MEDseq_fit`

, for fitting the models via EM/CEM.
`MEDseq_control`

allows supplying additional arguments which
govern, among other things, controls on the initialisation of the
allocations for the EM/CEM algorithm and the various model selection
options. `MEDseq_compare`

is provided for conducting model
selection between different results from using different covariate
combinations &/or initialisation strategies, etc.
`MEDseq_stderr`

is provided for computing the standard errors
of the coefficients for the covariates in the gating network.

A dedicated plotting function exists for visualising various aspects
of the results, using new methods as well as some existing methods
adapted from the **TraMineR** package. Finally, the package
also contains two data sets: `biofam`

and
`mvad`

.

## Installation

You can install the latest stable official release of the
`MEDseq`

package from CRAN:

`install.packages("MEDseq")`

or the development version from GitHub:

```
# If required install devtools:
# install.packages('devtools')
devtools::install_github('Keefe-Murphy/MEDseq')
```

In either case, you can then explore the package with:

```
library(MEDseq)
help(MEDseq_fit) # Help on the main modelling function
```

For a more thorough intro, the vignette document is available as
follows:

`vignette("MEDseq", package="MEDseq")`

However, if the package is installed from GitHub the vignette is not
automatically created. It can be accessed when installing from GitHub
with the code:

`devtools::install_github('Keefe-Murphy/MEDseq', build_vignettes = TRUE)`

Alternatively, the vignette is available on the package’s CRAN
page.

### References

Murphy, K., Murphy, T. B., Piccarreta, R., and Gormley, I. C. (2021).
Clustering longitudinal life-course sequences using mixtures of
exponential-distance models. *Journal of the Royal Statistical
Society: Series A (Statistics in Society)*, 184(4): 1414–1451.
<doi:10.1111/rssa.12712>.