Draw XmR Charts for NHS 'Making Data Count' Programme

Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS 'Making Data Count' programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

Compare Two Data Frames and Summarise the Difference

Easy comparison of two tabular data objects in R. Specifically designed to show differences between two sets of data in a useful way that should make it easier to understand the differences, and if necessary, help you work out how to remedy them. Aims to offer a more useful output than all.equal() when your two data sets do not match, but isn't intended to replace all.equal() as a way to test for equality.

Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Temporal Parametric Hazard Modeling

Provides native R implementations of the multiphase parametric hazard model of Blackstone, Naftel, and Turner (1986) with a focus on behavioral parity, transparent numerics, and reproducible validation against reference outputs from the original 'C'/'SAS' HAZARD program, originally developed at the University of Alabama at Birmingham (UAB). The 'SAS'/'C' code and this R package are currently developed and maintained at The Cleveland Clinic Foundation, and the R code was wholly developed at The Cleveland Clinic Foundation. The generalized temporal decomposition family extends to longitudinal mixed-effects settings (Rajeswaran et al. 2018 ). The package is intentionally implemented in pure R first; performance-critical paths may later be accelerated with 'Rcpp' without changing the public interface.

Geometrical Functionality of the 'spatstat' Family

Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)

Data Visualisation Functions for Epidemiological Data Science Products

Tools for making epidemiological reporting easier with consistent static and dynamic charts and maps. Builds on 'ggplot2' for static visualizations as described in Wickham (2016) and 'plotly' for interactive visualizations as described in Sievert (2020) .

Random Generation Functionality for the 'spatstat' Family

Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.

R Fortunes

A collection of fortunes from the R community.

Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Tools for Handling Spatial Objects

Please note that 'maptools' will be retired during October 2023, plan transition at your earliest convenience (see < https://r-spatial.org/r/2023/05/15/evolution4.html> and earlier blogs for guidance); some functionality will be moved to 'sp'. Set of tools for manipulating geographic data. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others.