Search Results - isPartOf:"Software"

neuRosim: An R Package for Generating fMRI Data

Welvaert, Marijke; Durnez, Joke; Moerkerke, Beatrijs; … - In: Journal of Statistical Software 44 (2011) i10

Studies that validate statistical methods for functional magnetic resonance imaging (fMRI) data often use simulated data to ensure that the ground truth is known. However, simulated fMRI data are almost always generated using in-house procedures because a well-accepted simulation method is...

Persistent link: https://www.econbiz.de/10010547841

Amelia II: A Program for Missing Data

Honaker, James; King, Gary; Blackwell, Matthew - In: Journal of Statistical Software 45 (2011) i07

Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives...

Persistent link: https://www.econbiz.de/10010547842

State of the Multiple Imputation Software

Yucel, Recai M. - In: Journal of Statistical Software 45 (2011) i01

researchers have been quite successful in developing the underlying software, documentation in a style that would be most … articles that illustrate these software developments. Here I provide a brief history of multiple imputation and relevant … software and highlight the contents of the contributions. Potential directions for the future of the software development is …

Persistent link: https://www.econbiz.de/10010547847

mritc: A Package for MRI Tissue Classification

Feng, Dai; Tierney, Luke - In: Journal of Statistical Software 44 (2011) i07

This paper presents an R package for magnetic resonance imaging (MRI) tissue classification. The methods include using normal mixture models, hidden Markov normal mixture models, and a higher resolution hidden Markov normal mixture model fitted by various optimization algorithms and by a...

Persistent link: https://www.econbiz.de/10010547849

Multiple Imputation by Chained Equations (MICE): Implementation in Stata

Royston, Patrick; White, Ian R. - In: Journal of Statistical Software 45 (2011) i04

Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in...

Persistent link: https://www.econbiz.de/10010547850

Statistical Simulation: Power Method Polynomials and Other Transformations

Demirtas, Hakan - In: Journal of Statistical Software 43 (2011) b02

Persistent link: https://www.econbiz.de/10010547852

Hidden Markov Models for Time Series : An Introduction Using R

Sanchez, Juana - In: Journal of Statistical Software 43 (2011) b04

Persistent link: https://www.econbiz.de/10010547853

Introduction to SamplerCompare

Thompson, Madeleine B. - In: Journal of Statistical Software 43 (2011) i12

SamplerCompare is an R package for comparing the performance of Markov chain Monte Carlo (MCMC) samplers. It samples from a collection of distributions with a collection of MCMC methods over a range of tuning parameters. Then, using log density evaluations per uncorrelated observation as a...

Persistent link: https://www.econbiz.de/10010547854

Multiple Imputation Using SAS Software

Yuan, Yang - In: Journal of Statistical Software 45 (2011) i06

Multiple imputation provides a useful strategy for dealing with data sets that have missing values. Instead of filling in a single value for each missing value, a multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the...

Persistent link: https://www.econbiz.de/10010547858

Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS

King, Martin D.; Calamente, Fernando; Clark, Chris A.; … - In: Journal of Statistical Software 44 (2011) i02

A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an...

Persistent link: https://www.econbiz.de/10010547861