The spdlog library is a widely-used and very capable header-only C++ library for logging. This package includes its headers as an R package to permit other R packages to deploy it via a simple LinkingTo: RcppSpdlog. As of version 0.0.9, it also provides both simple R logging functions and compiled functions callable by other packages.

This package provides visualizations for SHAP (SHapley Additive exPlanations) such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a shapviz object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages xgboost, lightgbm, fastshap, shapr, h2o, treeshap, DALEX, and kernelshap are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the shap package in Python, but there is no dependency on it.

This package provides a fast parallelized alternative to R's native dist function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of predefined distance functions from other R packages, as well as user- defined functions written in C++. For ease of use, the parDist function extends the signature of the dist function and uses the same parameter naming conventions as distance methods of existing R packages.

This package provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments.

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 lets you import foreign statistical formats into R via the ReadStat C library.

This package provides a general purpose toolbox for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Functions for simulating and testing particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics.

This package provides cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key.

This package provides qualitatively constrained (regression) smoothing splines via linear programming and sparse matrices.

Create and manage unique directories for each TensorFlow training run. This package provides a unique, time stamped directory for each run along with functions to retrieve the directory of the latest run or latest several runs.

This package provides an R interface to HiGHS, an optimization solver. It is designed for solving mixed-integer optimization problems with quadratic or linear objectives and linear constraints.

This package provides a comprehensive collection of color palettes, color maps, and tools to evaluate them.

This package lets you fit pedigree-based mixed-effects models.

This package provides functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e., a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

This package provides unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using the ggplot2 package.

The r-nleqslv package solves a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. There are options for using a numerical or user supplied Jacobian, for specifying a banded numerical Jacobian and for allowing a singular or ill-conditioned Jacobian.

This package provides methods and functions for fitting maximum likelihood models in R. This package modifies and extends the mle classes in the stats4 package.

This package provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the brglm2 package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration.

This package provides an interface to use SPARQL to pose SELECT or UPDATE queries to an end-point.

This is software accompanying the book 'Applied Smoothing Techniques for Data Analysis---The Kernel Approach with S-Plus Illustrations', Oxford University Press. It provides smoothing methods for nonparametric regression and density estimation

This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set cosmesis), and for censored data with competing risks (see data set menopause). The package also provides functions to visualize the observed data and the MLE.

This package provides data structures and algorithms for k-ary relations with arbitrary domains, featuring relational algebra, predicate functions, and fitters for consensus relations.

This package provides infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) SPSS and Stata files is provided. Further, the package produces tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to LaTeX and HTML.