This package provides a set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) doi:10.1007/s11336-011-9207-7 and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) doi:10.1111/bmsp.12070 for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre doi:10.1177/0146621613479818 for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.

There are three main goals to the vctrs package:

1. To propose vec_size() and vec_type() as alternatives to length() and class(). These definitions are paired with a framework for type-coercion and size-recycling.

2. To define type- and size-stability as desirable function properties, use them to analyse existing base function, and to propose better alternatives. This work has been particularly motivated by thinking about the ideal properties of c(), ifelse(), and rbind().

3. To provide a new vctr base class that makes it easy to create new S3 vectors. vctrs provides methods for many base generics in terms of a few new vctrs generics, making implementation considerably simpler and more robust.

This package provides an R interface to functions of the SAMtools library.

Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344)

This package provides functions for prior and likelihood sensitivity analysis in Bayesian models. It implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.

Query, set, and delete credentials from the git credential store. Manage GitHub tokens and other git credentials. This package is to be used by other packages that need to authenticate to GitHub and/or other git repositories.

The glmnet package provides efficient procedures for fitting the entire lasso or elastic-net regularization path for linear and Poisson regression, as well as logistic, multinomial, Cox, multiple-response Gaussian and grouped multinomial models. The algorithm uses cyclical coordinate descent in a path-wise fashion.

This package is a placeholder for the Bitstream Vera font. It is intended for the fontquiver package.

This package provides an implementation of interpreted string literals, inspired by Python's Literal String Interpolation (PEP-0498) and Docstrings (PEP-0257) and Julia's Triple-Quoted String Literals.

This package provides a set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

This package provides a collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future.

This package provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling.

This package lets you build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. The term "MARS" is trademarked and thus not used in the name of the package.

This package provides software for the book Spectral Analysis for Physical Applications, Donald B. Percival and Andrew T. Walden, Cambridge University Press, 1993.

This package is for genomic regions processing using command line tools such as BEDTools, BEDOPS and Tabix. These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging. The bedr package's API enhances access to these tools as well as offers additional utilities for genomic regions processing.

Customize Bootstrap and Bootswatch themes, like colors, fonts, grid layout, to use in Shiny applications, rmarkdown documents and flexdashboard.

This package provides a custom CSS/HTML or GIF/image file for the loading screen in R Shiny. It also can use the marquee to have a custom text loading screen.

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

This package provides miscellaneous functions to help customize ggplot2 objects. High-level functions are provided to post-process ggplot2 layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom geom, and helper functions to enforce symmetric scales or add tags to facetted plots.

This package provides an API for https://orcid.org. Functions include searching for people, searching by DOI, or searching by Orcid ID.

This package provides functions for the quality control, homogenization and missing data infilling of climatological series, and to obtain climatological summaries and grids from the results. Also functions to draw wind-roses and Walter&Lieth climate diagrams are included.

RestRserve is an R web API framework for building high-performance AND robust microservices and app backends. With Rserve backend on UNIX-like systems it is parallel by design. It will handle incoming requests in parallel - each request in a separate fork.

This package provides the usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models. Also, there are provided kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the evd package is provided, so that users can safely interchange most code.

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.