This package provides a collection of functions to implement a class for univariate polynomial manipulations.

This package provides a package for quantifying, profiling and removing cell free mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.

Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as glm. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.

This package performs search for the global minimum of a very complex non-linear objective function with a very large number of optima.

This is a companion package for the book "A Course in Statistics with R" (ISBN 978-1-119-15272-9.)

BASIX provides some efficient C/C++ implementations of native R procedures to speed up calculations in R.

This package provides means to run simulations for adaptive seamless designs with and without early outcomes for treatment selection and subpopulation type designs.

This package provides a tool to provide an easy, intuitive and consistent access to information contained in various R models, like model formulas, model terms, information about random effects, data that was used to fit the model or data from response variables. The package mainly revolves around two types of functions: Functions that find (the names of) information, starting with find_, and functions that get the underlying data, starting with get_. The package has a consistent syntax and works with many different model objects, where otherwise functions to access these information are missing.

This package provides primitives for visualizing distributions using ggplot2 that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized.

This package provides an implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available.

This package extends mlr3 with filter methods for feature selection. Besides standalone filter methods built-in methods of any machine-learning algorithm are supported. Partial scoring of multivariate filter methods is supported.

This package provides datasets to accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage.

This package performs 2D Delaunay triangulation, constrained or unconstrained, with the help of the C++ library CDT. A function to plot the triangulation is provided. The constrained Delaunay triangulation has applications in geographic information systems.

GLDEX offers fitting algorithms corresponding to two major objectives. One is to provide a smoothing device to fit distributions to data using the weighted and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, and L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

This package provides functions for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

This is a deprecated package for accessing huge amounts of data. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), or BufferedMatrix (Bioconductor). The main usage of it was inside the aroma.affymetrix package.

This package provides two methods of plotting categorical scatter plots such that the arrangement of points within a category reflects the density of data at that region, and avoids over-plotting.

Estimate a suite of normalizing transformations, including a new adaptation of a technique based on ranks which can guarantee normally distributed transformed data if there are no ties: ordered quantile normalization (ORQ). ORQ normalization combines a rank-mapping approach with a shifted logit approximation that allows the transformation to work on data outside the original domain. It is also able to handle new data within the original domain via linear interpolation. The package is built to estimate the best normalizing transformation for a vector consistently and accurately. It implements the Box-Cox transformation, the Yeo-Johnson transformation, three types of Lambert WxF transformations, and the ordered quantile normalization transformation. It estimates the normalization efficacy of other commonly used transformations, and it allows users to specify custom transformations or normalization statistics. Finally, functionality can be integrated into a machine learning workflow via recipes.

Read and write feather files, a lightweight binary columnar data store designed for maximum speed.

This package provides a vectorized R function for calculating probabilities from a standard bivariate normal CDF.

The Ziggurat generator for normally distributed random numbers, originally proposed by Marsaglia and Tsang (2000, https://doi.org/10.18637/jss.v005.i08) has been improved upon a few times starting with Leong et al (2005, https://doi.org/10.18637/jss.v012.i07). This package provides an aggregation for comparing different implementations in order to provide a 'faster but good enough' alternative for use with R and C++ code.

This package provides tools to compute marginal effects from statistical models and return the result as tidy data frames. These data frames are ready to use with the ggplot2 package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggeffect(). There is a generic plot() method to plot the results using ggplot2.

Unlike other tools that dynamically link to the Cairo stack, freetypeharfbuzz is statically linked to specific versions of the FreeType and harfbuzz libraries. This ensures deterministic computation of text box extents for situations where reproducible results are crucial (for instance unit tests of graphics).