Analyse data from longitudinal studies to characterise changes in values of semi-quantitative outcome variables within individual subjects, using high performance C++ code to enable rapid processing of large datasets. A flexible methodology is available for codifying these state transitions.
You can install the currently-released version from CRAN with this R command:
install.packages("Transition")Alternatively, you can install the latest development version of Transition from GitHub with:
# install.packages("devtools")
devtools::install_github("Mark-Eis/Transition")Authors: Mark C. Eisler and Ana V. Rabaza
eMail: Mark.Eisler@bristol.ac.uk, arabaza@pasteur.edu.uy
ORCID = 0000-0001-6843-3345, 0000-0002-9713-0797
Identify temporal transitions in test results for individual
subjects in a longitudinal study with
get_transitions().
Interpolate these transitions into a data frame for further
analysis with add_transitions().
Identify the previous test result for individual subjects and
timepoints in a longitudinal study with
get_prev_result().
Interpolate these previous test results into a data frame for
further analysis with add_prev_result().
Identify the previous test date for individual subjects and
timepoints in a longitudinal study
get_prev_date().
Interpolate these previous test dates into a data frame for
further analysis with add_prev_date().
Identify unique values for subjects, timepoints and test results
in longitudinal study data with uniques().
Transition uses high performance C++ code seamlessly
integrated into R using Rcpp to enable rapid
processing of large longitudinal study datasets.
While every effort is made to ensure this package functions as expected, the authors accept no responsibility for the consequences of errors.