Bayesian Marginal Effects for 'brms' Models
Calculate Bayesian marginal effects and average marginal effects for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6>).
methods, stats, data.table (>= 1.12.0), extraoperators (>=
0.1.1), brms, bayestestR, Rcpp, posterior
testthat (>= 3.0.0), covr, withr, knitr, rmarkdown, margins,
betareg

c(person(given = "Joshua F.",
family = "Wiley",
role = c("aut", "cre"),
email = "jwiley.psych@gmail.com",
comment = c(ORCID = "0000-0002-0271-6702")),
person(given = "Donald",
family = "Hedeker",
role = c("aut"),
email = "hedeker@uchicago.edu",
comment = c(ORCID = "0000-0001-8134-6094")))
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

Bayesian regularization for feedforward neural networks

brnn (Bayesian regularization for feed-forward neural networks)
Bayesian regularization for feed-forward neural networks

Bayesian Regularization for Feed-Forward Neural Networks
Bayesian regularization for feed-forward neural networks.

Broken Stick Model for Irregular Longitudinal Data
person("Stef", "van Buuren", email = "stef.vanbuuren@tno.nl", role = c("aut","cre"))
The broken stick model describes a set of individual curves by a
linear mixed model using a second-order linear B-spline. The main use of the model
is to align irregularly observed data to a user-specified grid of break ages.
All fitting can done in the Z-score scale, so non-linearity and irregular data
can be treated as separate problems. This package contains functions for fitting
a broken stick model to data, for predicting broken stick curves in new data,
and for plotting the broken stick estimates. For additional documentation on
background, methodology and applications of the broken stick model see
<https://stefvanbuuren.name/publications/2020_Brokenstick_JSS_manuscript.pdf>.
dplyr, lme4, matrixsampling, methods, rlang, splines, stats,
tidyr
AGD, ggplot2, grDevices, gridExtra, knitr, lattice, MASS,
Matrix, mice, mvtnorm, plyr, svglite, testthat, rmarkdown