msmtools introduces a fast and general method for restructuring classical longitudinal datasets into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the msm package.
# Install the released version from CRAN: install.packages( "msmtools" ) # Install the development version from GitHub: devtools::install_github( "contefranz/msmtools" )
msmtools comes with 4 functions:
augment(): the main function of the package. This is the workhorse which takes care of the data reshaping. It is very efficient and fast so highly dimensional datasets can be processed with ease;
polish(): it helps in find and remove those transition which occur at the same time but lead to different states within a given subject;
prevplot(): this is a plotting function which mimics the usage of
plot.prevalence.msm(), but with more things. Once you ran a multi-state model, use this function to plot a comparison between observed and expected prevalences;
survplot(): the aims of this function are double. You can use
survplot() as a plotting tool for comparing the empirical and the fitted survival curves. Or you can use it to build and get the datasets used for the plot. The function is based on msm
plot.survfit.msm(), but does more things and it is considerably faster.
For more information about msmtools, please check out the vignette with
vignette( "msmtools" ).
Bugs and issues can be reported at www.github.com/contefranz/msmtools/issues.