Input has to be in the form of vectors of lower class limits and upper class limits and frequencies; the output will give a cumulative frequency distribution table with cumulative frequency plot.

Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> . Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .

Nerd Fonts is a project that patches developer targeted fonts with a high number of glyphs (icons). Specifically to add a high number of extra glyphs from popular 'iconic fonts' such as Font Awesome, Devicons, Octicons, and others.

This plugin makes it simple to test general data, images, files, and numeric tables by saving expected data in a data directory (courtesy of pytest-datadir) that can be used to verify that future runs produce the same data.

one-more-re-nightmare is a regular expression engine that uses the technique presented in Regular-expression derivatives re-examined (Owens, Reppy and Turon, 2009; doi:10.1017/S0956796808007090) to interpret and compile regular expressions.

The uniseg package provides a tool to iterate over grapheme clusters. This may be used to determine the number of user-perceived characters, to split strings in their intended places, or to extract individual characters which form a unit.

This package provides a Shiny application for calculating phytosanitary inspection plans based on risks. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling).

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.

An implementation of the "FAST-9" corner detection algorithm explained in the paper FASTER and better: A machine learning approach to corner detection by Rosten E., Porter R. and Drummond T. (2008), available at <arXiv:0810.2434>. The package allows to detect corners in digital images.

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 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.

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.