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46
R2WinBUGS: A Package for Running WinBUGS from R
 JOURNAL OF STATISTICAL SOFTWARE
, 2005
"... The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the result ..."
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Cited by 45 (4 self)
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The R2WinBUGS package provides convenient functions to call WinBUGS from R. It automatically writes the data and scripts in a format readable by WinBUGS for processing in batch mode, which is possible since version 1.4. After the WinBUGS process has finished, it is possible either to read the resulting data into R by the package itself—which gives a compact graphical summary of inference and convergence diagnostics—or to use the facilities of the coda package for further analyses of the output. Examples are given to demonstrate the usage of this package.
MNP: R package for fitting multinomial probit models
 The Journal of Statistical Software 15, Issue
, 2005
"... MNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful includ ..."
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MNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005).
Responses to monetary policy shocks in the east and the west of europe: a comparison,” Center for Social and Economic Research 287
"... This paper compares impulse responses to monetary policy shocks in the euro area countries before the EMU and in the New Member States (NMS) from centraleastern Europe. We mitigate the smallsample problem, which is especially acute for the NMS, by using a Bayesian estimation that combines informati ..."
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This paper compares impulse responses to monetary policy shocks in the euro area countries before the EMU and in the New Member States (NMS) from centraleastern Europe. We mitigate the smallsample problem, which is especially acute for the NMS, by using a Bayesian estimation that combines information across countries. The impulse responses in the NMS are broadly similar to those in the euro area countries. There is some evidence that in the NMS, which have had higher and more volatile inflation, the Phillips curve is steeper than in the euro area countries. This finding is consistent with economic theory.
MCMC Methods for Multiresponse Generalized Linear Mixed Models: The MCMCglmm R Package
"... Generalized linear mixed models provide a flexible framework for modeling a range of data, although with nonGaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distribution ..."
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Generalized linear mixed models provide a flexible framework for modeling a range of data, although with nonGaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm, implements such an algorithm for a range of model fitting problems. More than one response variable can be analysed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zeroinflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in metaanalysis. All simulation is done in C / C++ using the CSparse library for sparse linear systems. If you use the software please cite this article, as published in the Journal of Statistic Software
Processing Polarity: How the ungrammatical intrudes on the grammatical
"... A central question in online human sentence comprehension is: how are linguistic relations established between different parts of a sentence? Previous work has shown that this dependency resolution process can be computationally expensive, but the underlying reasons for this are still unclear. We a ..."
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A central question in online human sentence comprehension is: how are linguistic relations established between different parts of a sentence? Previous work has shown that this dependency resolution process can be computationally expensive, but the underlying reasons for this are still unclear. We argue that dependency resolution is mediated by cuebased retrieval, constrained by independently motivated working memory principles defined in a cognitive architecture (ACTR). To demonstrate this, we investigate an unusual instance of dependency resolution, the processing of negative and positive polarity items, and confirm a surprising prediction of the cuebased retrieval model: partial cuematches—which constitute a kind of similaritybased interference—can give rise to the intrusion of ungrammatical retrieval candidates, leading to both processing slowdowns and even errors of judgment that take the form of illusions of grammaticality in patently ungrammatical structures. A notable achievement is that good quantitative fits are achieved without adjusting the key model parameters.
Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME
"... Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, socalled inverse mode ..."
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Cited by 7 (5 self)
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Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, socalled inverse modelling, is often the sole way of finding reasonable values for these parameters. There are many challenges involved in inverse model applications, e.g., the existence of nonidentifiable parameters, the estimation of parameter uncertainties and the quantification of the implications of these uncertainties on model predictions. The R˜package FME is a modeling package designed to confront a mathematical model with data. It includes algorithms for sensitivity and Monte Carlo analysis, parameter identifiability, model fitting and provides a Markovchain based method to estimate parameter confidence intervals. Although its main focus is on mathematical systems that consist of differential equations, FME can deal with other types of models. In this paper, FME is applied to a model describing the dynamics of the HIV virus. Note: The original version of this vignette has been published as Soetaert and Petzoldt
bcp: A Package for Performing a Bayesian Analysis of Change Point Problems. R package version 1.8.4, URL http://CRAN.Rproject.org
, 2007
"... Barry and Hartigan (1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry and Hartigan. We outline Barry and Hartigan’s approach and offer a new R package, pkgbcp (Erdman and Emerson 2007) ..."
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Barry and Hartigan (1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry and Hartigan. We outline Barry and Hartigan’s approach and offer a new R package, pkgbcp (Erdman and Emerson 2007), implementing their analysis. We discuss two frequentist alternatives to the Bayesian analysis, the recursive circular binary segmentation algorithm (Olshen and Venkatraman 2004) and the dynamic programming algorithm of (Bai and Perron 2003). We illustrate the application of bcp with economic and microarray data from the literature.
PyMC: Bayesian stochastic modelling in Python
 J. Stat. Softw
, 2010
"... This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques. ..."
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Cited by 4 (0 self)
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This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.
Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models
 J. Statistical Software
, 2010
"... This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP) models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis fo ..."
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This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP) models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of blackbox functions; and a new Monte Carlo method for inference in multimodal posterior distributions that combines simulated tempering and importance sampling. These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART), to treed Gaussian processes with jumps to the limiting linear model. It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007).
spTimer: SpatioTemporal Bayesian Modelling using R
, 2011
"... Hierarchical Bayesian modelling of large point referenced spacetime data are increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Bayesian model based analysis methods using the Markov chain Monte Carlo ( ..."
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Hierarchical Bayesian modelling of large point referenced spacetime data are increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Bayesian model based analysis methods using the Markov chain Monte Carlo (MCMC) techniques are, however, a very formidable task for large data sets rich in both space and time. Most such analyses are best performed using user written computer code in a low level language that takes hours and sometimes days to run in most personal computers. Currently there does not exist an R package which can fit and predict spacetime data effectively, although the package spBayes (Finley, et al., 2007)can analyse some moderately sized data sets. This paper develops the R package, spTimer specifically for modelling spatiotemporal data. This allows us to fit and forecast temporal data over large spatial domains even in midrange personal computers available today. The