Title: Another Look at the Acceptance-Rejection Method
Description: In mathematics, 'rejection sampling' is a basic technique used to generate observations from a distribution. It is also commonly called 'the Acceptance-Rejection method' or 'Accept-Reject algorithm' and is a type of Monte Carlo method. 'Acceptance-Rejection method' is based on the observation that to sample a random variable one can perform a uniformly random sampling of the 2D cartesian graph, and keep the samples in the region under the graph of its density function. Package 'AR' is able to generate/simulate random data from a probability density function by Acceptance-Rejection method. Moreover, this package is a useful teaching resource for graphical presentation of Acceptance-Rejection method. From the practical point of view, the user needs to calculate a constant in Acceptance-Rejection method, which package 'AR' is able to compute this constant by optimization tools. Several numerical examples are provided to illustrate the graphical presentation for the Acceptance-Rejection Method.

Title: Segmentation of an autoregressive Gaussian process of order 1
Author: S. Chakar, E. Lebarbier, C. Levy-Leduc, S. Robin
Description: 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

Title: A R Adaptive Multiple Importance Sampling
Depends: R (>= 2.15.0), MASS, LearnBayes, methods, graphics
Description: 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.

Title: Fitting Univariate Censored Linear Regression Model with
Author: Fernanda L. Schumacher, Victor H. Lachos and Christian E. Galarza
Maintainer: Fernanda L. Schumacher <fernandalschumacher@gmail.com>
Imports: mvtnorm, tmvtnorm, msm, numDeriv, utils, graphics, stats
Description: 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.