An R implementation for the Strain Elevation and
    Tension embedding algorithm from Bourne (2020)
    <doi:10.1007/s41109-020-00329-4>. The package embeds graphs and
    networks using the Strain Elevation and Tension embedding (SETSe)
    algorithm. SETSe represents the network as a physical system, where
    edges are elastic, and nodes exert a force either up or down based on
    node features. SETSe positions the nodes vertically such that the
    tension in the edges of a node is equal and opposite to the force it
    exerts for all nodes in the network. The resultant structure can then
    be analysed by looking at the node elevation and the edge strain and
    tension. This algorithm works on weighted and unweighted networks as
    well as networks with or without explicit node features.  Edge
    elasticity can be created from existing edge weights or kept as a
    constant.

A report of statistical findings (RSF) project template is
    generated using a 'bookdown' format. 'YAML' fields can be further
    customized. Additional helper functions provide extra features to the
    RSF.

Slow Feature Analysis (SFA), ported to R based on
    'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit'
    2.8 by Wolfgang Konen.

This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.

An R interface to the GeoRust crates 'geo' and 'geo-types' providing 
  access to geometry primitives and algorithms.

Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.