Models the direction of the maximum horizontal stress using
    relative plate motion parameters. Statistical algorithms to evaluate
    the modeling results compared with the observed data. Provides plots
    to visualize the results. Methods described in Stephan et al. (2023)
    <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998)
    <doi:10.1016/S0079-1946(98)00091-3>.

The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.

Implements methods to fit Virtual Twins models (Foster et al. 
  (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential
  effects in the context of clinical trials while controlling the probability
  of falsely detecting a differential effect when the conditional average
  treatment effect is uniform across the study population using parameter
  selection methods proposed in Wolf et al. (2022) 
  <doi:10.1177/17407745221095855>.

Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.

Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.

Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.