MCMC Sampling of Bayesian Linear Models via Summary Statistics (BayesSummaryStatLM)
Methods for generating Markov Chain Monte Carlo (MCMC) posterior samples of Bayesian linear regression model parameters that require only summary statistics of data as input. Summary statistics are useful for systems with very limited amounts of physical memory. The package provides two functions: one function that computes summary statistics of data and one function that carries out the MCMC posterior sampling for Bayesian linear regression models where summary statistics are used as input. The function read.regress.data.ff utilizes the R package ‘ff’ to handle data sets that are too large to fit into a user’s physical memory, by reading in data in chunks.
Abbreviating Questionnaires (or Other Measures) Using Genetic Algorithms (GAabbreviate)
The GAabbreviate uses Genetic Algorithms as an optimization tool to create abbreviated forms of lengthy questionnaires (or other measures) that maximally capture the variance in the original data of the long form of the measure.
A Suite of R Functions Implementing Spline Smoothing Techniques (assist)
A comprehensive package for fitting various non-parametric/semi-parametric linear/nonlinear fixed/mixed smoothing spline models.
Correlation of Bivariate Survival Times (SurvCorr)
Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze, Statistics in Medicine 2013. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.
Interpreting Time Series and Autocorrelated Data Using GAMMs (itsadug)
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package mgcv (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
Multiple Mediation Analysis (mma)
Used for general multiple mediation analysis. The analysis method is described in Yu et al. (2014).