Sensitivity Analysis for Observational Studies with Multiple Outcomes

Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) . Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.

Useful Tools for Structural Equation Modeling

Provides tools for structural equation modeling, many of which extend the 'lavaan' package; for example, to pool results from multiple imputations, probe latent interactions, or test measurement invariance.

Automatic Marking of R Assignments

Automatic marking of R assignments for students and teachers based on 'testthat' test suites.

Doubly Robust Inverse Probability Weighted Augmented GEE Estimator

Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).

Multiple Comparisons Using Normal Approximation

Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data.

Analysis of Multivariate Normal Datasets with Missing Values

An integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.

Various R Programming Tools for Plotting Data

Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as ('bandplot', 'wapply'), - enhanced versions of standard plots ('barplot2', 'boxplot2', 'heatmap.2', 'smartlegend'), - manipulating colors ('col2hex', 'colorpanel', 'redgreen', 'greenred', 'bluered', 'redblue', 'rich.colors'), - calculating and plotting two-dimensional data summaries ('ci2d', 'hist2d'), - enhanced regression diagnostic plots ('lmplot2', 'residplot'), - formula-enabled interface to 'stats::lowess' function ('lowess'), - displaying textual data in plots ('textplot', 'sinkplot'), - plotting dots whose size reflects the relative magnitude of the elements ('balloonplot', 'bubbleplot'), - plotting "Venn" diagrams ('venn'), - displaying Open-Office style plots ('ooplot'), - plotting multiple data on same region, with separate axes ('overplot'), - plotting means and confidence intervals ('plotCI', 'plotmeans'), - spacing points in an x-y plot so they don't overlap ('space').

Memory management in R by delayed assignments

Allows objects to be stored on disc and automatically recalled into memory, as required, by delayed assignment.

Assigning Ancestry Based on Population References

Assigns genetic ancestry to an individual and studies relationships between local and global populations.

Generalized Nonlinear Models

Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.