Variability Analysis in R (varian)
Uses a Bayesian model to estimate the variability in a repeated measure outcome and use that as an outcome or a predictor in a second stage model.
Full Text of ‘Scholarly’ Articles Across Many Data Sources (fulltext)
Provides a single interface to many sources of full text ‘scholarly’ data, including ‘Biomed Central’, Public Library of Science, ‘Pubmed Central’, ‘eLife’, ‘F1000Research’, ‘PeerJ’, ‘Pensoft’, ‘Hindawi’, ‘arXiv’ ‘preprints’, and more. Functionality included for searching for articles, downloading full or partial text, converting to various data formats used in and outside of R.
Import and Export Data (ImportExport)
Import and export data from the most common statistical formats by using R functions that guarantee the least loss of the data information, giving special attention to the date variables and the labelled ones.
Flexible Link-Based Survival Models (rstpm2)
R implementation of Stata’s stpm2 function (flexible link-based survival models), with extensions to different smoothers and penalised models.
Convolution-Based Nonstationary Spatial Modeling (convoSPAT)
Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the kriging predictor and standard errors, and create various plots to visualize nonstationarity.