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20
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 32 (2 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. Keywords: R, WinBUGS, interface, MCMC. An earlier version of this vignette has been published by the Journal of Statistical Software: Sturtz S, Ligges U, Gelman A (2005): “R2WinBUGS: A Package for Running WinBUGS from R.”
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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Cited by 9 (0 self)
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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
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 6 (4 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
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).
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.
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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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.
Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box
 Journal of Statistical Software
, 2009
"... Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned resi ..."
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Cited by 2 (1 self)
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Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates stateoftheart diagnostics that can be applied more generally and can be incorporated into the software of others.
Exploring an Adaptive Metropolis Algorithm
, 2010
"... While adaptive methods for MCMC are under active development, their utility has been underrecognized. We briefly review some theoretical results relevant to adaptive MCMC. We then suggest a very simple and effective algorithm to adapt proposal densities for random walk Metropolis and Metropolis adj ..."
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While adaptive methods for MCMC are under active development, their utility has been underrecognized. We briefly review some theoretical results relevant to adaptive MCMC. We then suggest a very simple and effective algorithm to adapt proposal densities for random walk Metropolis and Metropolis adjusted Langevin algorithms. The benefits of this algorithm are immediate, and we demonstrate its power by comparing its performance to that of three commonlyused MCMC algorithms that are widelybelieved to be extremely efficient. Compared to data augmentation for probit models, slice sampling for geostatistical models, and Gibbs sampling with adaptive rejection sampling, 1 the adaptive random walk Metropolis algorithm that we suggest is both more efficient and more flexible.
Adaptive Mixture of Studentt Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit
"... This introduction to the R package AdMit is a shorter version of Ardia et al. (2009), published in the Journal of Statistical Software. The package provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kern ..."
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This introduction to the R package AdMit is a shorter version of Ardia et al. (2009), published in the Journal of Statistical Software. The package provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Studentt distributions to the density of interest. Then, importance sampling or the independence chain MetropolisHastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the timeconsuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in an example of a bivariate bimodal distribution. Keywords: adaptive mixture, Studentt distributions, importance sampling, independence chain MetropolisHastings algorithm, Bayesian inference, R software. 1.