Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details.

Plots matrices of colours as grids of coloured squares - aka heatmaps, guaranteeing legible row and column names, without transformation of values, without re-ordering rows or columns, and without dendrograms.

This package provides functions that simplify calls to the Skilljar API. See <https://api.skilljar.com/docs/> for documentation on the Skilljar API. This package is not supported by Skilljar'.

This package produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.

This package implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.

This package provides tidyverse methods for wrangling and analyzing Earth Engine <https://earthengine.google.com/> data. These methods help the user with filtering, joining and summarising Earth Engine image collections.

Simulation of random vectors from truncated multivariate normal and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.

Base R sometimes requires verbose statements for simple, often recurring tasks, such as printing text without trailing space, ending with newline. This package aims at providing shorthands for such tasks.

Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).

By using RAINBOWR (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.

Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison.

Assessing the comparative performance of two logistic regression models or results of such models or classification models. Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) <doi:10.1373/clinchem.2011.167965> and Pencina et al. (2008) <doi:10.1002/sim.2929>.

This package provides a toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information.

The cnpy library written by Carl Rogers provides read and write facilities for files created with (or for) the NumPy extension for Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0.

This package provides lower-level functionality to interface with Google Cloud Platform tools. gcloud and gsutil are both supported. The functionality provided centers around utilities for the AnVIL platform.

This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits.

This package offers a quick and straight-forward way to explore and perform basic analysis of single cell sequencing data coming from droplet sequencing. It has been particularly tailored for Drop-seq.

This package provides functions used to build R packages. It locates compilers needed to build R packages on various platforms and ensures the PATH is configured appropriately so R can use them.

ExtRemes is a suite of functions for carrying out analyses on the extreme values of a process of interest; be they block maxima over long blocks or excesses over a high threshold.

This package provides tools to fit and predict with the high-dimensional principal fitted components model. This model is described by Cook, Forzani, and Rothman (2012) doi:10.1214/11-AOS962.

This package provides a collection of perceptually uniform color maps made by Peter Kovesi (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> at the Centre for Exploration Targeting (CET).

This package provides R bindings to the Sundown Markdown rendering library (https://github.com/vmg/sundown). Markdown is a plain-text formatting syntax that can be converted to XHTML or other formats.

CZMQ Ruby bindings, based on the generated low-level FFI bindings of CZMQ. The focus of of CZTop is on being easy to use and providing first class support for security mechanisms.

Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.