input matrices, which supports a broad variety of 39 predefined distance
functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
defined functions written in C++. For ease of use, the 'parDist' function
extends the signature of the 'dist' function and uses the same parameter
naming conventions as distance methods of existing R packages. The package
is mainly implemented in C++ and leverages the 'RcppParallel' package to
parallelize the distance computations with the help of the 'TinyThread'
library. Furthermore, the 'Armadillo' linear algebra library is used for
optimized matrix operations during distance calculations. The curiously
recurring template pattern (CRTP) technique is applied to avoid virtual
functions, which improves the Dynamic Time Warping calculations while
the implementation stays flexible enough to support different DTW step
Suggests: dtw, ggplot2, proxy, highlight, testthat, RcppArmadillo,
Packaged: 2017-09-27 19:10:16 UTC; Alexander

Suggests: dtw, ggplot2, proxy, testthat, RcppArmadillo, RcppXPtrUtils

person("Alexander", "Eckert", role = c("aut", "cre"), email = "info@alexandereckert.com"),
person("Lucas", "Godoy", role = "ctb", email = "lucasdac.godoy@gmail.com"),
person("Srikanth", "KS", role = "ctb", email = "sri.teach@gmail.com")
input matrices, which supports a broad variety of 41 predefined distance

Title: Methods for combining independent subset Markov chain Monte
Carlo (MCMC) posterior samples to estimate a posterior density
Description: Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.