  In \textit{computer-based testing} it has become standard to collect response
accuracy (RA) and response times (RTs) for each test item. IRT models are used
to measure a latent variable (e.g., ability, intelligence) using the RA
observations. The information in the RTs can help to improve routine operations
in (educational) testing, and provide information about speed of working. In
modern applications, the joint models are needed to integrate RT information in
a test analysis. The R-package LNIRT supports fitting joint models through a
user-friendly setup which only requires specifying RA, RT data, and the total
number of Gibbs sampling iterations. More detailed specifications of the
analysis are optional. The main results can be reported through the summary
functions, but output can also be analysed with Markov chain Monte Carlo (MCMC)
output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package
is illustrated with two real data applications.
