This package provides a function that performs gene-permuting of a gene-set enrichment analysis (GSEA) calculation with or without the absolute filtering. Without filtering, users can perform (original) two-tailed or one-tailed absolute GSEA.

This package lets you interact with Google Sheets through the Sheets API v4. This package can read and write both the metadata and the cell data in a Sheet.

This crate provides a library implementation of the standard library's old scoped_thread_local! macro for providing scoped access to thread local storage (TLS) so any type can be stored into TLS.

This crate provides a library implementation of the standard library's old scoped_thread_local! macro for providing scoped access to thread local storage (TLS) so any type can be stored into TLS.

Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) <doi:10.1007/s00184-024-00949-1>.

JDemetra+ (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed with JDemetra+ can be stored into workspaces. JWSACruncher (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that re-estimates all the multi-processing defined in a workspace and to export the result. rjwsacruncher allows to launch easily the JWSACruncher'.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers. The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

An R-Shiny module containing a "markdownInput". This input allows the user to write some markdown code and to preview the result. This input has been inspired by the "comment" window of <https://github.com/>.

This package provides a single function plotting Marradi's trees: a graphical representation of a numerical variable for comparing the variable mean and standard deviation across subgroups. See A. Marradi "L'analisi monovariata" (1993, ISBN: 9788820496876).

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.

This package provides functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. Please refer to the documentation of the boot.power.anova() function for further details.

Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, PKbioanalysis provides a relational infrastructure for plate management and injection sequence.

This package provides tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.

Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose.

AWS-LC is a general-purpose cryptographic library maintained by the AWS Cryptography team for AWS and their customers. It is based on code from the Google BoringSSL project and the OpenSSL project.

AWS-LC is a general-purpose cryptographic library maintained by the AWS Cryptography team for AWS and their customers. It is based on code from the Google BoringSSL project and the OpenSSL project.

A pure-python universal errors-and-erasures Reed-Solomon Codec, based on the tutorial at Wikiversity. This is a burst-type implementation, so that it supports any Galois field higher than 2^3, but not binary streams.

BlankSlate provides an abstract base class with no predefined methods (except for __send__ and __id__). BlankSlate is useful as a base class when writing classes that depend upon method_missing (e.g. dynamic proxies).

Classical Boson Sampling using the algorithm of Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for generating random unitary matrices, evaluation of matrix permanents (both real and complex) and evaluation of complex permanent minors.

Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.

Returns a Hasse diagram of the layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918173> or the restricted layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918174> of an experimental design.

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).

The raw dataset and model used in Lai et al. (2021) Decoupled responses of native and exotic tree diversities to distance from old-growth forest and soil phosphorous in novel secondary forests. Applied Vegetation Science, 24, e12548.