This package provides a kernel module that is capable of resetting hardware devices into a state where they can be re-initialized or passed through into a virtual machine (VFIO). While it would be great to have these in the kernel as PCI quirks, some of the reset procedures are very complex and would never be accepted as a quirk (ie AMD Vega 10).

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

ISAAC (Indirection, Shift, Accumulate, Add, and Count) is a fast pseudo-random number generator. It is suitable for applications where a significant amount of random data needs to be produced quickly, such as solving using the Monte Carlo method or for games. The results are uniformly distributed, unbiased, and unpredictable unless you know the seed.

This package implements the same interface as Math::Random::ISAAC.

This library extends the Rust standard collections to return a result when an allocation error occurs, ala RFC 2116. The API currently proposes a fallible interface for Vec, Box, Arc, Btree and Rc, as well as a TryClone trait which is implemented for primitive Rust traits and a fallible format macro.

This library extends the Rust standard collections to return a result when an allocation error occurs, ala RFC 2116. The API currently proposes a fallible interface for Vec, Box, Arc, Btree and Rc, as well as a TryClone trait which is implemented for primitive Rust traits and a fallible format macro.

This package implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>. See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.

The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in python'. This package aims to make these tools accessible to R users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and Mayer, Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.

This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed. Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices. Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments" Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.

Automatically builds 20 classification models from data. The package returns 26 plots, 5 tables and a summary report. The package automatically builds 12 individual classification models, including error (RMSE) and predictions. That data is used to create an ensemble, which is then modeled using 8 methods. The process is repeated as many times as the user requests. The mean of the results are presented in a summary table. The package returns the confusion matrices for all 20 models, tables of the correlation of the numeric data, the results of the variance inflation process, the head of the ensemble and the head of the data frame.

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

This package provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such a sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine (based on information criteria) among those which allow for such a conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets.

This package provides common verification against SVG11-DTD for railroad.

Sys functions for the Rust bindings of the javacriptcore library.

This package provides trait to facilitate interoperatibility with libxcb C API.

This package provides an API for parsers and writers of various RDF formats.

This package provides the ability to use Rust declarative macros as proc_macro attributes or derives.

Superluminal Performance C API bindings.

Macros for derive-visitor package.

Index tables for Korean character encodings.

Configure Sequoia using a configuration file.

Configure Sequoia using a configuration file.

This package provides Emscripten bindings for glutin.

This package provides a scheduled thread pool.

Documentation at https://melpa.org/#/typewriter-roll-mode