An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.

Provides predictive microbiology model functions and convenience
    wrappers for fitting primary growth, microbial inactivation, dynamic,
    omnibus, and cardinal parameter models to experimental data using nonlinear
    least squares and related mixed-effects or time-varying workflows. Includes
    helper functions for extracting fitted values, calculating model diagnostics,
    and comparing fitted models. Implemented model families include those
    described by: Zwietering et al. (1990)
    <doi:10.1128/AEM.56.6.1875-1881.1990>, Baranyi and Roberts (1994)
    <doi:10.1016/0168-1605(94)90157-0>, Baranyi and Roberts (1995)
    <doi:10.1016/0168-1605(94)00121-L>, Buchanan et al. (1997)
    <doi:10.1006/fmic.1997.0125>, Richards (1959)
    <doi:10.1093/jxb/10.2.290>, Fang et al. (2012)
    <doi:10.1111/j.1750-3841.2012.02873.x>, Fang et al. (2013)
    <doi:10.1016/j.fm.2012.12.005>, Huang (2008)
    <doi:10.1111/j.1750-3841.2008.00785.x>, Huang (2009)
    <doi:10.1016/j.jfoodeng.2008.07.011>, Huang (2013)
    <doi:10.1016/j.foodcont.2012.11.019>, Geeraerd et al. (2005)
    <doi:10.1016/j.ijfoodmicro.2004.11.038>, van Boekel (2002)
    <doi:10.1016/S0168-1605(01)00742-5>, Peleg (1999)
    <doi:10.1016/S0963-9969(99)00081-2>, Mafart et al. (2002)
    <doi:10.1016/S0168-1605(01)00624-9>, Albert and Mafart (2005)
    <doi:10.1016/j.ijfoodmicro.2004.10.016>, Rosso et al. (1993)
    <doi:10.1006/jtbi.1993.1099>, Rosso et al. (1995)
    <doi:10.1128/AEM.61.2.610-616.1995>, and Rosso et al. (1996)
    <doi:10.4315/0362-028X-59.9.944>.

Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.