Q-Q and Manhattan Plots for GWAS Data

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

Various Plotting Functions

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Utility Functions for 'spatstat'

Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes.

Turner Miscellaneous

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

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'.

Turn Vectors and Lists of Vectors into Indexed Structures

Package designed for working with vectors and lists of vectors, mainly for turning them into other indexed data structures.

1000 Genomes Project Metadata

Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) and Byrska-Bishop et al. (2022) , and raw data is available at < http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/>. See Turner (2022) for more details.

Vector Field Visualizations with 'ggplot2'

A 'ggplot2' extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.

Extended Generalised Linear Hidden Markov Models

Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson (1998) , and Rolf Turner (2008) and the references cited therein.

Linear Networks Functionality of the 'spatstat' Family

Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. 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. Random point patterns on a network can be generated using a variety of models.