Author: Paulino Perez Rodriguez, Daniel Gianola
Description: Bayesian regularization for feedforward neural networks

Title: brnn (Bayesian regularization for feed-forward neural networks)
Description: Bayesian regularization for feed-forward neural networks

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

Depends: R (>= 3.5.0), Formula, truncnorm

Title: Broken Stick Model for Irregular Longitudinal Data
Authors@R: person("Stef", "van Buuren", email = "stef.vanbuuren@tno.nl", role = c("aut","cre"))
Description: 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
Imports: dplyr, lme4, matrixsampling, methods, rlang, splines, stats,
Suggests: AGD, ggplot2, grDevices, gridExtra, knitr, lattice, MASS,
Matrix, mice, mvtnorm, plyr, svglite, testthat, rmarkdown

background, methodology and applications see
Imports: coda, dplyr, lme4, matrixsampling, methods, rlang, splines,

Suggests: AGD, bookdown, ggplot2, grDevices, gridExtra, knitr, lattice,
MASS, Matrix, mice, mvtnorm, plyr, svglite, testthat, rmarkdown

Description: Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times.
Specification of the model requires just three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random.
The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects.
The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. For additional documentation on background, methodology and applications see <https://growthcharts.org/brokenstick/articles/manual/manual.html>.