Multivariate data analysis is the simultaneous observation of more than one characteristic. In contrast to the analysis of univariate data, in this approach not only a single variable or the relation between two variables can be investigated, but the relations between many attributes can be considered. For the statistical analysis of chemical data one has to take into account the special structure of this type of data. This package contains about 30 functions, mostly for regression, classification and model evaluation and includes some data sets used in the R help examples. It was designed as a R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

This package provides the header files of mio, a cross-platform C++11 header-only library for memory mapped file IO.

This package provides a wrapper around the C++ library polylabel from Mapbox, providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon.

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.

Asio is a cross-platform C++ library for network and low-level I/O programming that provides developers with a consistent asynchronous model using a modern C++ approach. It is also included in Boost but requires linking when used with Boost. Standalone it can be used header-only (provided a recent compiler). Asio is written and maintained by Christopher M. Kohlhoff, and released under the Boost Software License', Version 1.0.

This package provides various tools for creating iterators, many patterned after functions in the Python itertools module, and others patterned after functions in the snow package.

Webshot makes it easy to take screenshots of web pages from within R. It can also run Shiny applications locally and take screenshots of the application; and it can render and screenshot static as well as interactive R Markdown documents.

This package guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.

The R kernel for the Jupyter environment executes R code which the front-end (Jupyter Notebook or other front-ends) submits to the kernel via the network.

This is a package for graphical and statistical analyses of environmental data, with a focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. It provides major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. It comes with numerous built-in data sets from regulatory guidance documents and environmental statistics literature. It includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, https://link.springer.com/book/10.1007/978-1-4614-8456-1).

This package provides tools for the estimation and simulation of latent variable models.

This package extends the fitdistr function of the MASS package with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left-censored, right-censored and interval-censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.

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.

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.

This package provides an easy way to fill an environment with active bindings that call a C++ function.

This package provides a reticulate wrapper for the Python package anndata. It provides a scalable way of keeping track of data and learned annotations. It is used to read from and write to the h5ad file format.

This package provides tools to create a measure of inter-point dissimilarity useful for clustering mixed data, and, optionally, perform the clustering.

This package implements time series clustering along with optimized techniques related to the dynamic time warping distance and its corresponding lower bounds. The implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.

This package provides functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.

This package provides smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017). Differently from quantreg, the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in the mgcv package, but fits non-parametric quantile regression models.

This package contains routines and documentation for solving quadratic programming problems.

This package contains the data set for the crowd-sourced benchmarks from running the benchmarkme package.

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>.