Helm "Switch-to-REPL" offers the helm-switch-to-repl action, a generalized and extensible version of helm-ff-switch-to-shell. It can be added to helm-find-files and other helm-type-file sources such as helm-locate.

This piece of code sets up some reader macros that make it simpler to input string literals which contain backslashes and double quotes This is very useful for writing complicated docstrings and, as it turns out, writing code that contains string literals that contain code themselves.

The MicrobiomeBenchmarkData package provides functionality to access microbiome datasets suitable for benchmarking. These datasets have some biological truth, which allows to have expected results for comparison. The datasets come from various published sources and are provided as TreeSummarizedExperiment objects. Currently, only datasets suitable for benchmarking differential abundance methods are available.

This Action handles doing automatic method dispatching for REST requests. It takes a normal Catalyst action, and changes the dispatch to append an underscore and method name. First it will try dispatching to an action with the generated name, and failing that it will try to dispatch to a regular method.

Fast, flexible framework for implementing Bayesian optimization of model hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>. The package allows the user to run scoring function in parallel, save intermediary results, and tweak other aspects of the process to fully utilize the computing resources available to the user.

This is a data package for normalised homosapien data downloaded from DEE2. The package both downloads, normalises, and filters the data, and provides a way to access the data from a canonical store without needing local processing. This package was built as a way to generate and store canonical test data for CellScore.

This package provides different functionalities and calculations used in the world of basketball to analyze the statistics of the players, the statistics of the teams, the statistics of the quintets and the statistics of the plays. For more details of the calculations included in the package can be found in the book Basketball on Paper written by Dean Oliver.

The Python requests library bundles the urllib3 library, however, some software distributions modify requests to remove the bundled library. This makes some operations difficult, such as suppressing the “insecure platform warning” messages that urllib emits. This package is a simple library to find the correct path to exceptions in the requests library regardless of whether they are bundled or not.

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.

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).

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 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 package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage.

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 gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest.

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.

Ember for Rails 3.1+

Roberts2005Annotation Annotation Data (Roberts2005Annotation) assembled using data from public repositories.

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