Structures for Preference Data

Convenient structures for creating, sourcing, reading, writing and manipulating ordinal preference data. Methods for writing to/from PrefLib formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) .

Convert and Aggregate Bibliographies

Authors working with 'LaTeX' articles use the built-in bibliography options and 'BibTeX' files. While this might work with 'LaTeX', it does not function well with Web articles. As a way out, 'rebib' offers tools to convert and combine bibliographies from both sources.

Converting 'LaTeX' 'R Journal' Articles into 'RJ-web-articles'

Articles in the 'R Journal' were first authored in 'LaTeX', which performs admirably for 'PDF' files but is less than ideal for modern online interfaces. The 'texor' package does all the transitional chores and conversions necessary to move to the online versions.

Delaunay Triangulation and Dirichlet (Voronoi) Tessellation

Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways. Clips tessellations to sub-windows. Calculates perimeters of tessellations. Summarises information about the tiles of the tessellation. Calculates the centroidal Voronoi (Dirichlet) tessellation using Lloyd's algorithm.

Functions to Perform Isotonic Regression

Linear order and unimodal order (univariate) isotonic regression; bivariate isotonic regression with linear order on both variables.

Robust and User-Friendly Analysis of Growth and Fluorescence Curves

High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable 'R' data containers. A 'shiny' application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) .

Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Q-Q and Manhattan Plots for GWAS Data

Create Q-Q and manhattan plots for GWAS data from PLINK results.

Gower's Distance

Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.

Turner Miscellaneous

Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data.