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Utility Functions for 'spatstat'
Contains utility functions for the 'spatstat' family of packages which may also be useful for other purposes.
Perform Set Operations on Vectors, Automatically Generating All n-Wise Comparisons, and Create Markdown Output
Automates set operations (i.e., comparisons of overlap) between multiple vectors. It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
Sparse Three-Dimensional Arrays and Linear Algebra Utilities
Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms.
The Self-Controlled Case Series Method
Various self-controlled case series models used to investigate associations between time-varying exposures such as vaccines or other drugs or non drug exposures and an adverse event can be fitted. Detailed information on the self-controlled case series method and its extensions with more examples can be found in Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y. (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) and < https://sccs-studies.info/index.html>.
Visualization and Analysis of Statistical Measures of Confidence
Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis
of confidence region simulations, (3) calculating confidence intervals and the associated
actual coverage for binomial proportions, (4) calculating the support values and the
probability mass function of the Kaplan-Meier product-limit estimator, and (5) plotting
the actual coverage function associated with a confidence interval for the survivor
function from a randomly right-censored data set. Each is given in greater detail next.
(1) Plots the two-dimensional confidence region for probability distribution parameters
(supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif,
weibull) corresponding to a user-given complete or right-censored dataset and level of
significance. The crplot() algorithm plots more points in areas of greater curvature to
ensure a smooth appearance throughout the confidence region boundary. An alternative
heuristic plots a specified number of points at roughly uniform intervals along its boundary.
Both heuristics build upon the radial profile log-likelihood ratio technique for plotting
confidence regions given by Jaeger (2016)
Identify Distributions that Match Reported Sample Parameters (SPRITE)
The SPRITE algorithm creates possible distributions of discrete responses
based on reported sample parameters, such as mean, standard deviation and range
(Heathers et al., 2018,
Poly-Omic Prediction of Complex TRaits
It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.
Power Analysis for Meta-Analysis
A simple and effective tool for computing and visualizing statistical power for meta-analysis,
including power analysis of main effects (Jackson & Turner, 2017)
Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants
A companion to the World Checklist of Vascular Plants (WCVP). It includes functions to generate maps and species lists, as well as match names to the WCVP. For more details and to cite the package, see: Brown M.J.M., Walker B.E., Black N., Govaerts R., Ondo I., Turner R., Nic Lughadha E. (in press). "rWCVP: A companion R package to the World Checklist of Vascular Plants". New Phytologist.
Pipeline for Topological Data Analysis
A comprehensive toolset for any
useR conducting topological data analysis, specifically via the
calculation of persistent homology in a Vietoris-Rips complex.
The tools this package currently provides can be conveniently split
into three main sections: (1) calculating persistent homology; (2)
conducting statistical inference on persistent homology calculations;
(3) visualizing persistent homology and statistical inference.
The published form of TDAstats can be found in Wadhwa et al. (2018)