This package provides miscellaneous functions commonly used in other packages maintained by Yihui Xie.

This package provides a collection of clean R Markdown HTML document templates using classless CSS styles. These documents use a minimal set of dependencies but still look great, making them suitable for use a package vignettes or for sharing results via email.

This is a package for constructing minimum-cost regular spanning subgraph as part of a non-parametric two-sample test for equality of distribution.

This package provides functions for robust principal component analysis (PCA) by projection pursuit.

This package provides type-stable rolling window functions over any R data type. Cumulative and expanding windows are also supported. For more advanced usage, an index can be used as a secondary vector that defines how sliding windows are to be created.

Geometry shapes in R are typically represented by matrices (points, lines), with more complex shapes being lists of matrices (polygons). Geometries will convert various R objects into these shapes. Conversion functions are available at both the R level, and through Rcpp.

This package provides a differential evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that

1. do not sacrifice simplicity of design,

2. are essentially tuning-free, and

3. can be efficiently implemented directly in the R language.

This package provides a toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models.

This package provides tools to create some specific Space-Filling Design (SFD) and to test their quality.

This package implements core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with DE matrices and count matrices, a collection of functions for manipulating and plotting data via ggplot2, and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP, collapsing vertices of each cluster in the graph, and propagating graph labels.

This package is a micro-package for getting your IP address, either the local/internal or the public/external one. Currently only IPv4 addresses are supported.

This package provides bindings to the OSQP solver. The OSQP solver is a numerical optimization package or solving convex quadratic programs written in C and based on the alternating direction method of multipliers. See <arXiv:1711.08013> for details.

This package provides a collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort. Second, these shortcut functions are generic, and can be applied not only to vectors, but also to other objects as well. The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models, mixed effects models and Bayesian models.

This package provides a collection of algorithms and functions to aid statistical modeling. It includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. It also includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.

This package provides color palettes that have been generated mostly from Wes Anderson movies.

This package provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. It is similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.

This package provides a reticulate wrapper for the Python package anndata. It provides a scalable way of keeping track of data and learned annotations. It is used to read from and write to the h5ad file format.

Miscellaneous functions commonly used by YuLab-SMU, such as install_zip_gh to install R packages from Github ZIP files.

This package provides a means to mock a package function, i.e., temporarily substitute it for testing. It was designed as a drop-in replacement for the now deprecated testthat::with_mock() and testthat::local_mock().

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This package provides smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017). Differently from quantreg, the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in the mgcv package, but fits non-parametric quantile regression models.

This package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.

This package contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e., the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997).