BMS: Bayesian Model Averaging Library

Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priorss include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilitites, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.

hbsae: Hierarchical Bayesian Small Area Estimation

Functions to compute small area estimates based on a basic area or unit-level model. The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. In the latter case numerical integration is used to average over the posterior density for the between-area variance. The output includes the model fit, small area estimates and corresponding MSEs, as well as some model selection measures. Additional functions provide means to compute aggregate estimates and MSEs, to minimally adjust the small area estimates to benchmarks at a higher aggregation level, and to graphically

commandr: Command pattern in R

An S4 representation of the Command design pattern. The Operation class is a simple implementation using closures and supports forward and reverse (undo) evaluation. The more complicated Protocol framework represents each type of command (or analytical protocol) by a formal S4 class. Commands may be grouped and consecutively executed using the Pipeline class. Example use cases include logging, do/undo, analysis pipelines, GUI actions, parallel processing, etc.

granovaGG: Graphical Analysis of Variance Using ggplot2

This collection of functions in granovaGG provides what we call elemental graphics for display of anova results. The term elemental derives from the fact that each function is aimed at construction of graphical displays that afford direct visualizations of data with respect to the fundamental questions that drive the particular anova methods. This package represents a modification of the original granova package; the key change is to use ggplot2, Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkinson). The main

ipdmeta: Tools for subgroup analyses with multiple trial data using aggregate statistics

This package provides functions to estimate an IPD linear mixed effects model for a continuous outcome and any categorical covariate from study summary statistics. There are also functions for estimating the power of a treatment-covariate interaction test in an individual patient data meta-analysis from aggregate data.

GB2: Generalized Beta Distribution of the Second Kind: properties, likelihood, estimation

GB2 is a simple package that explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulae for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted using the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the vizualization and

kSamples: K-Sample Rank Tests and their Combinations

Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, and Steel's multiple comparison test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test it also combines these tests across several blocks of samples. Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Steel's test is inverted to provide

NetComp: Network Generation and Comparison

This package contains functions to carry out high throughput data analysis and to conduct data set comparisons. Similarity matrices from high throughput phenotypic data containing uninformative (e.g. wild type) or missing data can be calculated to report similarity of response. A suite of graph comparisons using an adjacency or correlation matrix format are included to facilitate quick network analysis.

fmt: Variance estimation of FMT method (Fully Moderated t-statistic)

This package computes posterior residual variances to be used in the denominator of a moderated t-statistic from a linear model analysis of microarray data. It is an extension of the moderated t-statistic original proposed by Smyth (2004). LOESS local regression and empirical Bayesian method are used to estimate gene specific prior degrees of freedom and prior variance based on average gene intensity level. The posterior residual variance in the denominator is a weighted average of prior and residual variance and the weights are prior degrees

rbenchmark: Benchmarking routine for R

rbenchmark is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.

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