This package provides a file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. safetensors also enables cross language and cross frameworks compatibility making it an ideal format for storing machine learning model weights.

This package infers directional Conservative causal core (gene) networks (C3NET). This is a version of the algorithm C3NET with directional network.

This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.

This is an extension to Shiny that brings interactions and animation effects from the jQuery UI library.

This package implements Barzilai-Borwein spectral methods for solving nonlinear system of equations, and for optimizing nonlinear objective functions subject to simple constraints.

This package provides a set of functions to analyze overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).

The mlr3 package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core mlr3 packages.

This package provides a dependency manager for R projects that allows you to manage the R packages your project depends on in an isolated, portable, and reproducible way.

This package provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to:

1. Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions.

2. Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws.

3. Provide various summaries of draws in convenient formats.

4. Provide lightweight implementations of state of the art posterior inference diagnostics.

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.

This package lets you create a reproducible ggplot2 object by storing the data and calls.

This package provides miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.

This is a package supporting cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; doi:10.1007/s11336-009-9125-0). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Performs unconditional exact tests and power calculations for 2x2 contingency tables. For comparing two independent proportions, performs Barnard's test (1945) using the original CSM test (Barnard (1947)), using Fisher's p-value referred to as Boschloo's test (1970), or using a Z-statistic (Suissa and Shuster (1985)). For comparing two binary proportions, performs unconditional exact test using McNemar's Z-statistic (Berger and Sidik (2003)), using McNemar's Z-statistic with continuity correction, or using CSM test. Calculates confidence intervals for the difference in proportion.

Fit Bayesian generalized (non-)linear multivariate multilevel models using Stan for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.

This package provides data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals.

This package contains the program ttf2pt1, for use with the extrafont package.

This package implements reinforcement learning environments and algorithms as described in Sutton & Barto (1998). The Q-Learning algorithm can be used with function approximation, eligibility traces (Singh & Sutton, 1996) and experience replay (Mnih et al., 2013).

This package provides a collection of functions that perform operations on time-series accelerometer data, such as identify the non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented.

This package provides a minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates.

The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.

This package implements list environments. List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting.

This package is a ggplot2 extension. It provides some utility functions that do not entirely fit within the grammar of graphics concept. The package extends ggpplots facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. It also allows multiple colour, fill scales per plot and hosts a smaller collection of stats, geoms and axis guides.

This package provides an implementation of an algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of ucminf is designed for easy interchange with the package optim.