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>).

The mixed model for repeated measures (MMRM) is a popular
  model for longitudinal clinical trial data with continuous endpoints,
  and 'brms' is a powerful and versatile package for fitting Bayesian
  regression models.  The 'brms.mmrm' R package leverages 'brms' to run
  MMRMs, and it supports a simplified interfaced to reduce difficulty
  and align with the best practices of the life sciences.  References:
  Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008)
  <doi:10.1177/009286150804200402>.

Bayesian regularization for feed-forward neural networks.

Implements efficient 'NumPy'-like broadcasted operations for atomic and recursive arrays.
    In the context of operations involving 2 (or more) arrays,
    “broadcasting” refers to efficiently recycling array dimensions,
    without making copies.
		Besides linking to 'Rcpp',
		'broadcast' does not use any external libraries in any way;
		'broadcast' was essentially made from scratch and can be installed out-of-the-box.
		The implementations available in 'broadcast' include, but are not limited to, the following.
		  1) Broadcasted element-wise operations on any 2 arrays;
		  they support a large set of
		  relational, arithmetic, Boolean, string, and bit-wise operations.
		  2) A faster, more memory efficient, and broadcasted abind-like function,
		  for binding arrays along an arbitrary dimension.
		  3) Broadcasted ifelse-like and apply-like functions.
		  4) Casting functions,
		  that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa.
		  5) A few linear algebra functions for statistics.
		The functions in the 'broadcast' package strive to minimize computation time and memory usage
		(which is not just better for efficient computing, but also for the environment).