WebAssembly Micro Runtime (WAMR) is a lightweight standalone WebAssembly (Wasm) runtime with small footprint, high performance and highly configurable features for applications cross from embedded, IoT, edge to Trusted Execution Environment (TEE), smart contract, cloud native and other features.

This package implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363> Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with C++'.

This package aims to simplify the writing process, especially for Dutch legal authors. It has also been implemented in English and can be expanded to include other languages. The package offers macros for typical legal structures and contains a referencing system.

For those wishing to interact with the Charles Schwab Individual Trader API (<https://developer.schwab.com/products/trader-api--individual>) with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process.

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.

This package provides tools to create a layout for figures made of multiple panels, and to fill the panels with base, lattice', ggplot2 and ComplexHeatmap plots, grobs, as well as content from all image formats supported by ImageMagick (accessed through magick').

This package is a collection of search spaces for hyperparameter optimization in the mlr3 ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets.

Unlike other tools that dynamically link to the Cairo stack, freetypeharfbuzz is statically linked to specific versions of the FreeType and harfbuzz libraries. This ensures deterministic computation of text box extents for situations where reproducible results are crucial (for instance unit tests of graphics).

This package allows simple reflection of expressions containing variables. Reflection here means that a Haskell expression is turned into a string. The primary aim of this package is teaching and understanding; there are no options for manipulating the reflected expressions beyond showing them.

When writing a large manuscript, it is sometimes beneficial to repeat a theorem (or lemma or...) at an earlier or later point for didactic purposes. Unlike thmtools, this package allows replicating theorems not only in the same document, but in any other file.

Fits mixtures of multivariate contaminated normal distributions (with eigen-decomposed scale matrices) via the expectation conditional- maximization algorithm under a clustering or classification paradigm Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.

This package provides tools for simplifying the creation and management of data structures suitable for dealing with policy portfolios, that is, two-dimensional spaces of policy instruments and policy targets. The package also allows to generate measures of portfolio characteristics and facilitates their visualization.

This Python library is a wrapper around tokenize from the Python standard library. It provides two additional tokens ESCAPED_NL and UNIMPORTANT_WS, and a Token data type. Use src_to_tokens and tokens_to_src to roundtrip.

The package generalises the macro patching commands provided by P. Lehmann's etoolbox. The difference between this package and its sibling xpatch is that this package sports a very powerful \regexpatchcmd based on the l3regex module of the LaTeX3 experimental packages.

This package provides functions for making contour plots. The contour plot can be created from grid data, a function, or a data set. If non-grid data is given, then a Gaussian process is fit to the data and used to create the contour plot.

Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.

This package provides a progress bar similar to dplyr that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using knitr or rmarkdown', and you still want to see progress of calculations in the terminal.

repl-utilities is a set of utilities which ease life at the REPL. It includes three sorts of features: introspective procedures, miscellaneous utility functions, and, pulling them together, methods to conveniently keep these symbols and optionally additional symbols available in whichever package you switch to.

It contains some example datasets used in bibliometrix'. The data are bibliographic datasets exported from the SCOPUS (<https://scopus.com>) and Clarivate Analytics Web of Science (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package bibliometrix (<https://bibliometrix.org>).

Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.