This package fits the MRS algorithm for comparison across probability distributions. The model is based on a nonparametric process taking the form of a Markov model that transitions between a “null” and an “alternative” state on a multi-resolution partition tree of the sample space. MRS effectively detects and characterizes a variety of underlying differences. These differences can be visualized using several plotting functions.
The development version can be installed from GitHub on Linux, macOS, or Windows:
install.packages("remotes")
remotes::install_github("MaStatLab/MRS")The main functions and their descriptions are provided in the help files:
ans = mrs(X, G)
ans = andova(X, G, H)
summary(ans)
plot1D(ans)
plot2D(ans)
plotTree(ans)
plot1DSigWindows(ans)The package includes three reproducible demonstrations of the main workflows:
demo(package = "MRS")
demo("mrs_1d", package = "MRS", ask = FALSE)
demo("mrs_2d", package = "MRS", ask = FALSE)
demo("andova_replicates", package = "MRS", ask = FALSE)The former standalone scripts in examples/ have been
consolidated into these demos. Each workflow now has one purpose:
mrs_1d shows a localized difference, its signed effect,
and Bayesian-FDR selection.mrs_2d compares the observed grouping with a
label-shuffled control before showing multiscale probability, effect,
and tree views.andova_replicates compares pooled MRS with both ANDOVA
integration methods, uses an explicit baseline, and contrasts nested
studies with 4, 10, and 20 replicates to show why pooled analysis can be
overconfident.All simulations use fixed random seeds. The obsolete
mrs_nested() examples are represented by the supported
andova() workflow, while developer-oriented numerical
diagnostics are covered by the automated tests.
Soriano J. and Ma L. (2017). Probabilistic multi-resolution scanning for two-sample differences. Journal of the Royal Statistical Society: Series B, 79(2), 547-572. https://doi.org/10.1111/rssb.12180
Ma L. and Soriano J. (2018). Analysis of distributional variation through multi-scale Beta-Binomial modeling. Journal of Computational and Graphical Statistics, 27(3), 529-541. https://doi.org/10.1080/10618600.2017.1402774