This package provides functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. It enables the estimation of the DINA and DINO model, the multiple group (polytomous) GDINA model, the multiple choice DINA model, the general diagnostic model (GDM), the structured latent class model (SLCA), and regularized latent class analysis. See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) doi:10.18637/jss.v074.i02 for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, doi:10.20982/tqmp.11.3.p189) as well as Ravand and Robitzsch (2015).

This package provides a set of predicates and assertions for checking the properties of matrices. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

int64 values can be created and accessed via the bit64 package and its integer64 class which package the int64 representation cleverly into a double. The nanotime package builds on this to support nanosecond-resolution timestamps. This package helps conversions between R and C++ via several helper functions provided via a single header file. A complete example client package is included as an illustration.

This package provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. It additionally supplies a modelling function (wdm) and further methods for the resulting object.

This package provides a collection of tools to make working with physical measurements easier. One can convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.

This package implements tools for weighted network visualization and analysis, as well as Gaussian graphical model computation. It contains graph plotting methods, and tools for psychometric data visualization and graphical model estimation. See Epskamp et al. (2012) doi:10.18637/jss.v048.i04.

This is a framework for construction and analysis of 2D Monte-Carlo simulations. In addition, this package includes various distributions.

This package lets you use syntax inspired by the package glue to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.

Several fast random number generators are provided as C++ header-only libraries: the PCG family as well as Xoroshiro128+ and Xoshiro256+. Additionally, fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+ and Xoshiro256+ as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011) as provided by the package sitmo.

This package provides pure R tools to read BMP format images. It is currently limited to 8 bit greyscale images and 24, 32 bit (A)RGB images.

This package is a r-ggplot2 extension that provides flipped components:

1. horizontal versions of r-ggplot2 stats and r-ggplot2 geoms;

2. vertical versions of r-ggplot2 positions.

This package implements the Differential Evolution algorithm. This algorithm is used for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of DifferentialEvolution in DEoptim interfaces with C code for efficiency.

This package lets you take formulas including random-effects components (formatted as in lme4, glmmTMB, etc.) and process them. It includes various helper functions.

This package provides the exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. The package also gives easy access to the underlying C routines through an API; see the package vignette for details.

The pls package implements multivariate regression methods: Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Canonical Powered Partial Least Squares (CPPLS). It supports:

• several algorithms: the traditional orthogonal scores (NIPALS) PLS algorithm, kernel PLS, wide kernel PLS, Simpls, and PCR through svd

• multi-response models (or PLS2)

• flexible cross-validation

• Jackknife variance estimates of regression coefficients

• extensive and flexible plots: scores, loadings, predictions, coefficients, (R)MSEP, R², and correlation loadings

• formula interface, modelled after lm(), with methods for predict, print, summary, plot, update, etc.

• extraction functions for coefficients, scores, and loadings

• MSEP, RMSEP, and R² estimates

• multiplicative scatter correction (MSC)

The r-mhsmm package implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Also, this package is suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.

Users may want to align plots with associated information that requires axes to be exactly matched in subplots, e.g. hierarchical clustering with a heatmap. This package provides utilities to align associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.

This package provides miscellaneous functions commonly used in other packages maintained by Yihui Xie.

This package fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.

This package is a micro-package for getting your IP address, either the local/internal or the public/external one. Currently only IPv4 addresses are supported.

This package provides a replacement and extension of the optim function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that the function optimr was prepared to simplify the incorporation of minimization codes going forward. This package also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.

This package provides tools for measuring inequality, concentration, and poverty measures. It provides both empirical and theoretical Lorenz curves.

Assertthat is an extension to stopifnot() that makes it easy to declare the pre and post conditions that your code should satisfy, while also producing friendly error messages so that your users know what they've done wrong.

Least Angle Regression ("LAR") is a model selection algorithm; a useful and less greedy version of traditional forward selection methods. A simple modification of the LAR algorithm implements Tibshirani's Lasso; the Lasso modification of LARS calculates the entire Lasso path of coefficients for a given problem at the cost of a single least squares fit. Another LARS modification efficiently implements epsilon Forward Stagewise linear regression.