The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the 'BFI' methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2025) <doi:10.48550/arXiv.2404.17464>; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) <doi:10.1017/rsm.2025.6>.

Provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms, see
 Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.

Implementation of default Bayes factors
    for testing statistical hypotheses under various statistical models. The package is
    intended for applied quantitative researchers in the
    social and behavioral sciences, medical research,
    and related fields. The Bayes factor tests can be
    executed for statistical models such as 
    univariate and multivariate normal linear models,
    correlation analysis, generalized linear models, special cases of 
    linear mixed models, survival models, relational
    event models. Parameters that can be tested are
    location parameters (e.g., group means, regression coefficients),
    variances (e.g., group variances), and measures of 
    association (e.g,. polychoric/polyserial/biserial/tetrachoric/product
    moments correlations), among others.
    Relevant references on the methodology The statistical underpinnings are
    described in
    O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>,
    Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>,
    Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>,
    Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
    Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>,
    Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>,
    Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>,
    Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and
    Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the
    packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18>
    and the relevant methodological papers.