Helper functions for TUFLOW FV models. Current functionality includes reading in and plotting output POINTS files and generating initial conditions based on point observations.

Common tree-sitter parsing tools for R. It is meant to be used by other packages that specialize in particular languages and file formats.

Variable importance rankings depend on the method, the random seed and the resample used to compute them. This package treats every source of importance as a judge expressing a ranking over the predictors, and synthesises those rankings into a Kemeny median ranking with ties. Uncertainty about the consensus is quantified through bootstrap rank confidence sets, top-k probabilities and clustering of disagreeing judges.

This package implements safe policy learning under regression discontinuity designs with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>. The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The rdlearn package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses.

Allows one to use Osmium Tool (<https://osmcode.org/osmium-tool/>) from R. Osmium is a multipurpose command line tool that enables one to manipulate and analyze OpenStreetMap files through several different commands. Currently, this package does not aim to offer functions that cover the entire Osmium API, instead making available functions that wrap only a very limited set of its features.

This package provides tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .

Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).

NCL (NCAR Command Language) is one of the most popular spatial data mapping tools in meteorology studies, due to its beautiful output figures with plenty of color palettes designed by experts <https://www.ncl.ucar.edu/index.shtml>. Here we translate all NCL color palettes into R hexadecimal RGB colors and provide color selection function, which will help users make a beautiful figure.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

RankMap is a fast and scalable tool for reference-based cell type annotation of single-cell and spatial transcriptomics data. It uses ranked gene expression and multinomial regression to achieve robust predictions, even with partial gene coverage. Compatible with Seurat, SingleCellExperiment, and SpatialExperiment objects, RankMap offers flexible preprocessing and significantly faster runtime than tools like SingleR, Azimuth, and RCTD.

This package provides an interface to Mapzen'-based APIs (including geocode.earth, Nextzen, and NYC GeoSearch) for geographic search and geocoding, isochrone calculation, and vector data to draw map tiles. See <https://www.mapzen.com/documentation/> for more information. The original Mapzen has gone out of business, but rmapzen can be set up to work with any provider who implements the Mapzen API.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).

R package for wavelet variance and correlation decompositions of genomic signals, averaging across chromosomes (or more generally any set of multiple temporal or spatial signals).

This package provides some basic linear algebra functionality for sparse matrices. It includes Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.

This package provides a set of S3 generics to axe components of fitted model objects and help reduce the size of model objects saved to disk.

This R package caches the results of a function so that when you call it again with the same arguments it returns the pre-computed value.

This package provides a fast and user-friendly implementation of nonparametric estimators for censored event history (survival) analysis with the Kaplan-Meier and Aalen-Johansen methods.

This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. It provides estimates for each gene within each sample.

Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file.

Bayesian estimation of multilevel Vector Autoregression (VAR) models using Stan. Supports Gaussian, Binary, and Ordinal (adjacent category) outcome variables with random effects and customizable priors.

Data about the bakers, challenges, and ratings for "The Great British Bake Off", from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.

This tool performs pairwise correlation analysis and estimate causality. Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

Checks that students have the correct version of R', R packages, RStudio and other dependencies installed, and that the recommended RStudio configuration has been applied.

Estimation of changepoints using an "S-curve" approximation. Formation of confidence intervals for changepoint locations and magnitudes. Both abrupt and gradual changes can be modeled.