Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application 
    of choice models in R. Users are able to write their own 
    model functions or use a mix of already available ones. Random heterogeneity, 
    both continuous and discrete and at the level of individuals and 
    choices, can be incorporated for all models. There is support for both standalone 
    models and hybrid model structures.  Both classical 
    and Bayesian estimation is available, and multiple discrete 
    continuous models are covered in addition to discrete choice. 
    Multi-threading processing is supported for estimation and a large
    number of pre and post-estimation routines, including for computing posterior
    (individual-level) distributions are available. 
    For examples, a manual, and a support forum, visit 
    <https://www.ApolloChoiceModelling.com>. For more information on choice 
    models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, 
    S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview 
    of the field.

Produces several metrics to assess the prediction of ordinal 
    categories based on the estimated probability distribution for each unit of analysis
    produced by any model returning a matrix with these probabilities.

Corrects gas-chromatography peak areas for the influence of ambient
    air pressure on standard detectors open to the ambient atmosphere, such as
    the flame ionization detector, whose pressure sensitivity was
    characterised by Bocek, Novak and Janak (1969)
    <doi:10.1016/S0021-9673(00)99223-9>. Unlike the pressure compensation of
    Ayers and Clardy (1985) <https://patents.google.com/patent/US4512181A>, which
    is combined with a calibration and valid only for a single calibration period
    of a few days, per-cylinder peak areas are decomposed by principal components
    into a pressure-correlated component and per-peak drift; a common
    pressure-sensitivity coefficient (kappa) is estimated with a heavy-tail-robust
    fit on a drift-reduced signal, and slow drift plus a daily factor are removed.
    Returns the corrected areas together with a chi-square goodness-of-fit
    diagnostic. Structural-break detection (package 'strucchange', Zeileis and
    others (2002) <doi:10.18637/jss.v007.i02>) is provided for episode-level and
    variance breakpoint analysis.