Segmentation of an autoregressive Gaussian process of order 1
S. Chakar, E. Lebarbier, C. Levy-Leduc, S. Robin
This package corresponds to the implementation of the robust approach for estimating change-points in the mean of an AR(1) Gaussian process by using the methodology described in the paper arXiv 1403.1958

A R Adaptive Multiple Importance Sampling
R (>= 2.15.0), MASS, LearnBayes, methods, graphics
The ARAMIS package runs the Adaptive Multiple Importance
Sampler (AMIS) algorithm from Jean-Marie Cornuet, Jean-Michel
Marin, Antonietta Mira and Christian Robert (2012), Adaptive
Multiple Importance Sampling, Scandinavian Journal of
Statistics. The Code is flexible enough to accommodate
virtually any specification of the target and proposal
distribution together with any clustering routine for the
adaptation step and any initialization procedure.

Fitting Univariate Censored Linear Regression Model with
Autoregressive Errors
Fernanda L. Schumacher, Victor H. Lachos and Christian E. Galarza
mvtnorm, tmvtnorm, msm, numDeriv, utils, graphics, stats
It fits an univariate left or right censored linear regression model
with autoregressive errors under the normal distribution. It provides estimates
and standard errors of the parameters, prediction of future observations and
it supports missing values on the dependent variable. It also provides convergence
plots when exists at least one censored observation.

It fits an univariate left or right censored linear regression model
with autoregressive errors under the normal distribution. It provides estimates
and standard errors of the parameters, prediction of future observations and
it supports missing values on the dependent variable.
It also performs influence diagnostic through local influence for three possible
perturbation schemes.

Autoregressive Cokriging Models for Multifidelity Codes
c(person("Pulong", "Ma", role=c("aut", "cre"), email="mpulong@gmail.com"))
For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2019) <arXiv:1910.10225>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) <arXiv:1909.01836>.
Rcpp, mvtnorm (>= 1.0-10), stats, methods, ggplot2