This package provides templates for reports in rmarkdown and functions to create tables and summaries of data.

Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.

Use Rmarkdown First method to build your package. Start your package with documentation, functions, examples and tests in the same unique file. Everything can be set from the Rmarkdown template file provided in your project, then inflated as a package. Inflating the template copies the relevant chunks and sections in the appropriate files required for package development.

This package implements the statistic FAVA, an Fst-based Assessment of Variability across vectors of relative Abundances, as well as a suite of helper functions which enable the visualization and statistical analysis of relative abundance data. The FAVA R package accompanies the paper, â Quantifying compositional variability in microbial communities with FAVAâ by Morrison, Xue, and Rosenberg (2025) <doi:10.1073/pnas.2413211122>.

This package provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. fddm implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in C++ providing faster speed than existing packages.

This package provides a collection of datasets essential for functional genomic analysis. Gene names, gene positions, cytoband information, sourced from Ensembl and phenotypes association graph prepared from GWAScatalog are included. Data is available in both GRCh37 and 38 builds. These datasets facilitate a wide range of genomic studies, including the identification of genetic variants, exploration of genomic features, and post-GWAS functional analysis.

This package provides a toolbox to derive flexible cutoffs for fit indices in Covariance-based Structural Equation Modeling based on the paper by Niemand & Mai (2018) <doi:10.1007/s11747-018-0602-9>. Flexible cutoffs are an alternative to fixed cutoffs - rules-of-thumb - regarding an appropriate cutoff for fit indices such as CFI or SRMR'. It has been demonstrated that these flexible cutoffs perform better than fixed cutoffs in grey areas where misspecification is not easy to detect. The package provides an alternative to the tool at <https://flexiblecutoffs.org> as it allows to tailor flexible cutoffs to a given dataset and model, which is so far not available in the tool. The package simulates fit indices based on a given dataset and model and then estimates the flexible cutoffs. Some useful functions, e.g., to determine the GoF- or BoF-nature of a fit index, are provided. So far, additional options for a relative use (is a model better than another?) are provided in an exploratory manner.

Extends the fitdist() (from fitdistrplus') adding the Anderson-Darling ad.test() (from ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from stats package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from mclust').

Bayesian estimation of forced choice models in Item Response Theory using rstan (See Stan Development Team (2020) <https://mc-stan.org/>).

Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

This package provides a persistent datastore for fiery apps. The datastore is build on top of the storr package and can thus be based on a variety of backends. The datastore contains both a global and session-scoped section.

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce fibos', an R and Python package that extends the OS methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.

This package provides a collection of functions designed to retrieve, filter and spatialize data from the Catálogo Taxônomico da Fauna do Brasil. For more information about the dataset, please visit <http://fauna.jbrj.gov.br/fauna/listaBrasil/>.

An interface to the core Familias functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad (2016) <doi:10.1016/j.fsigen.2016.04.005>.

This package provides functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <doi:10.48550/arXiv.1903.11696>.

This package implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.

This package provides tools, helpers and data structures for developing models and time series functions for fable and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.

Interface to Palantir Foundry', including reading and writing structured or unstructured datasets, and more <https://www.palantir.com/platforms/foundry/>.

Use R as a minimal build system. This might come in handy if you are developing R packages and can not use a proper build system. Stay away if you can (use a proper build system).

Application of the filtered monotonic polynomial (FMP) item response model to flexibly fit item response models. The package includes tools that allow the item response model to be build on any monotonic transformation of the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.

This package provides functions for finding smooth interpolating curves connecting a series of points in the plane. Curves may be open or closed, that is, with the first and last point of the curve at the initial point.

This package provides a collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.

An R interface for generating features for a cohort using data in the Common Data Model. Features can be constructed using default or custom made feature definitions. Furthermore it's possible to aggregate features and get the summary statistics.

One can easily draw the membership function of f(x,y) by package FuzzyNumbers.Ext.2 in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package FuzzyNumbers for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.