Converts vectors of numbers into character vectors of numerals, including cardinals (one, two, three) and ordinals (first, second, third). Supports negative numbers, fractions, and arbitrary-precision integer and high-precision floating-point vectors provided by the bignum package.

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

The lipid scrambling activity of protein extracts and purified scramblases is often determined using a fluorescence-based assay involving many manual steps. flippant offers an integrated solution for the analysis and publication-grade graphical presentation of dithionite scramblase assays, as well as a platform for review, dissemination and extension of the strategies it employs. The package's name derives from a play on the fact that lipid scrambling is also sometimes referred to as flipping'. The package is originally published as Cotton, R.J., Ploier, B., Goren, M.A., Menon, A.K., and Graumann, J. (2017). "flippantâ An R package for the automated analysis of fluorescence-based scramblase assays." BMC Bioinformatics 18, 146. <DOI:10.1186/s12859-017-1542-y>.

Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix.

Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See <https://lrberge.github.io/fplot/> for more information.

Routines for forecasting univariate time series using Theta Models.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to install and load the entire package-collection with a single R command, and provides convenient ways to find relevant documentation. Most commonly, you should not worry about the individual packages that make up the package-collection as you can access all features via this package. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to implement analyses of plot species distributions, topography, demography, and biomass. It also includes a torus translation test to determine habitat associations of tree species as described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.

Query data hosted in Microsoft Fabric'. Provides helpers to open DBI connections to SQL endpoints of Lakehouse and Data Warehouse items; submit Data Analysis Expressions ('DAX') queries to semantic model datasets in Microsoft Fabric and Power BI'; read Delta Lake tables stored in OneLake ('Azure Data Lake Storage Gen2'); and execute Spark code via the Livy API'.

Provide a range of plugins for fiery web servers that handle different aspects of server-side web security. Be aware that security cannot be handled blindly, and even though these plugins will raise the security of your server you should not build critical infrastructure without the aid of a security expert.

This package implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.

This package provides a collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2> and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze bivariate and multivariate data sets of financial returns.

The fastai <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at fast.ai', including out of the box support for vision, text, tabular, audio, time series, and collaborative filtering models.

This package provides a tool to explore wide data sets, by detecting, ranking and plotting groups of statistically dependent columns.

This package provides a collection of utility functions for manipulating and analyzing factor vectors in R. It offers tools for filtering, splitting, combining, and reordering factor levels based on various criteria. The package is designed to simplify common tasks in categorical data analysis, making it easier to work with factors in a flexible and efficient manner.

Function factories are functions that make functions. They can be confusing to construct. Straightforward techniques can produce functions that are fragile or hard to understand. While more robust techniques exist to construct function factories, those techniques can be confusing. This package is designed to make it easier to construct function factories.

This package contains four main functions (i.e., four pieces of furniture): table1() which produces a well-formatted table of descriptive statistics common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.

This package implements the Fourier cumulative sum (CUSUM) cointegration test for detecting cointegration relationships in time series data with structural breaks. The test uses Fourier approximations to capture smooth structural changes and CUSUM statistics to test for cointegration stability. Based on methodology described in Zaghdoudi (2025) <doi:10.46557/001c.144076>. The corrected Akaike Information Criterion (AICc) is used for optimal frequency selection.

Uses raw vectors to minimize memory consumption of categorical variables with fewer than 256 unique values. Useful for analysis of large datasets involving variables such as age, years, states, countries, or education levels.

R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.

Finds CRAN packages by the topic requested. The topic can be given as a character string or as a regular expression and will help users to locate CRAN packages matching their specified requirement. findPackage(<string>) returns a data frame of packages with description containing the input string.

Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.

Efficient algorithms for performing, updating, and removing rows or columns from the QR decomposition, R decomposition, or the inverse of the R decomposition of a matrix as rows or columns are added or removed. It also includes functions for solving linear systems of equations, normal equations for linear regression models, and normal equations for linear regression with a RIDGE penalty. For a detailed introduction to these methods, the monograph Matrix Computations (2013, <doi:10.1007/978-3-319-05089-8>) for complete introduction to the methods.

This package provides three methods to generate fully-sequential space-filling designs inside a unit hypercube. A fully-sequential space-filling design means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.