Supports Bayesian models with full and partial (hence
    arbitrary) dependencies between random variables. Discrete and continuous
    variables are supported, and conditional joint probabilities and probability
    densities are estimated using Kernel Density Estimation (KDE). The full
    general form, which implements an extension to Bayes' theorem, as well as
    the simple form, which is just a Bayesian network, both support regression
    through segmentation and KDE and estimation of probability or relative
    likelihood of discrete or continuous target random variables. This package
    also provides true statistical distance measures based on Bayesian models.
    Furthermore, these measures can be facilitated on neighborhood searches,
    and to estimate the similarity and distance between data points.
    Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
    and by Scutari (2010) <doi:10.18637/jss.v035.i03>.

Computes robust and bias-corrected sandwich variance estimators for multi-state Cox models with clustered time-to-event data. Also provides Wald tests for heterogeneity, generalized least-squares linear trends, and order-restricted trends among transition-specific coefficients. The methodology extends the marginal Cox model bias-correction framework of Wang et al. (2023) <doi:10.1002/bimj.202200113> to the multi-state setting.

Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

Early insights in probability theory were largely influenced by
    questions about gambling and games of chance, as noted by Blitzstein and
    Hwang (2019, ISBN:978-1138369917). In modern times, playing cards continue
    to serve as an effective teaching tool for probability, statistics, and even
    'R' programming, as demonstrated by Grolemund (2014, ISBN:978-1449359010).
    The 'mmcards' package offers a collection of utility functions designed to
    aid in the creation, manipulation, and utilization of playing card decks in
    multiple formats. These include a standard 52-card deck, as well as
    alternative decks such as decks defined by custom anonymous functions and
    custom interleaved decks. Optimized for the development of educational
    'shiny' applications, the package is particularly useful for teaching
    statistics and probability through card-based games. Functions include
    shuffle_deck(), which creates either a shuffled standard deck or a shuffled
    custom alternative deck; deal_card(), which takes a deck and returns a list
    object containing both the dealt card and the updated deck; and i_deck(),
    which adds image paths to card objects, further enriching the package's
    utility in the development of interactive 'shiny' application card games.