This package provides an API for efficient .hic file data extraction with programmatic matrix access. It doesn't store the pointer data for all the matrices, only the one queried, and currently it only supports matrices.

This package fits generalized linear models efficiently using RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner to help safeguard against convergence issues.

This package provides interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are:

• Feature importance described by Fisher et al. (2018),

• accumulated local effects plots described by Apley (2018),

• partial dependence plots described by Friedman (2001),

• individual conditional expectation ('ice') plots described by Goldstein et al. (2013) https://doi.org/10.1080/10618600.2014.907095,

• local models (variant of 'lime') described by Ribeiro et. al (2016),

• the Shapley Value described by Strumbelj et. al (2014) https://doi.org/10.1007/s10115-013-0679-x,

• feature interactions described by Friedman et. al https://doi.org/10.1214/07-AOAS148 and tree surrogate models.

This is a package for estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The brglmFit fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>.

This package enables you to estimate the p-values for predictors x against target variable y in Lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic.

This package provides a computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.

This package provides an R interface to the nanoarrow C library and the Apache Arrow application binary interface. Functions to import and export ArrowArray, ArrowSchema, and ArrowArrayStream C structures to and from R objects are provided alongside helpers to facilitate zero-copy data transfer among R bindings to libraries implementing the Arrow C data interface.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.

Read large text files by splitting them in smaller files. This package also provides some convenient wrappers around fread() and fwrite() from package data.table.

This package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) pre-whitening approaches to determining trends in climate data.

This package provides gradient projection algorithms for factor rotation. For details see ?GPArotation.

This package provides functions to compare a model object to a comparison object. If the objects are not identical, the functions can be instructed to explore various modifications of the objects (e.g., sorting rows, dropping names) to see if the modified versions are identical.

This package enables the use of emoji and the Font Awesome glyphs in both base and ggplot2 graphics.

This package provides implementations of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear models.

This package provides functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.

This package provides methods for calculating accurate numerical first and second order derivatives.

In S3 generics, it's useful to take ... so that methods can have additional arguments. But this flexibility comes at a cost: misspelled arguments will be silently ignored. The ellipsis package is an experiment that allows a generic to warn if any arguments passed in ... are not used.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

This package provides tools for multiple imputation of missing data in multilevel modeling. It includes a user-friendly interface to the packages pan and jomo, and several functions for visualization, data management and the analysis of multiply imputed data sets.

This is a supplement to the maps package providing the larger and/or higher-resolution databases.

This package provides key-value stores with automatic pruning. Caches can limit either their total size or the age of the oldest object (or both), automatically pruning objects to maintain the constraints.

This package provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

This is a package for developers to check user-supplied function arguments. It is designed to be simple, fast and customizable. Error messages follow the tidyverse style guide.

This package provides a collection of tools to streamline the process of fitting elastic net models with glmnet. In addition to providing a formula interface, it also features a function cva.glmnet to do crossvalidation for both α and λ, as well as some utility functions.