Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2019 or higher). It also includes a function to visualize pit stop performance.

Estimates the probability matrix for the RÃ C Ecological Inference problem using the Expectation-Maximization Algorithm with four approximation methods for the E-Step, and an exact method as well. It also provides a bootstrap function to estimate the standard deviation of the estimated probabilities. In addition, it has functions that aggregate rows optimally to have more reliable estimates in cases of having few data points. For comparing the probability estimates of two groups, a Wald test routine is implemented. The library has data from the first round of the Chilean Presidential Election 2021 and can also generate synthetic election data. Methods described in Thraves, Charles; Ubilla, Pablo; Hermosilla, Daniel (2024) A Fast Ecological Inference Algorithm for the RÃ C case <doi:10.2139/ssrn.4832834>.

This package provides a parametrization framework for finite mixture distribution using S4 objects. Density, cumulative density, quantile and simulation functions are defined. Currently normal, Tukey g-&-h, skew-normal and skew-t distributions are well tested. The gamma, negative binomial distributions are being tested.

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.

This package provides a fold change rank based method is presented to search for genes with changing expression and to detect recurrent chromosomal copy number aberrations. This method may be useful for high-throughput biological data (micro-array, sequencing, ...). Probabilities are associated with genes or probes in the data set and there is no problem of multiple tests when using this method. For array-based comparative genomic hybridization data, segmentation results are obtained by merging the significant probes detected.

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 three S7 classes â streamline, bundle, and bundle_set â for representing diffusion MRI tractography data in R, together with a concise set of methods for computing shape descriptors (arc-length, curvature, torsion, sinuosity), the Hausdorff distance between streamlines, arc-length reparametrization of streamlines and bundles onto uniform grids, combination of streamlines or bundles into a single bundle, combination of bundles from multiple subjects or sessions into a bundle_set, and coercion to and from the dwiFiber S4 class of the dti package. See Dell'Acqua, F., Descoteaux, M. and Leemans, A. (2024) "Handbook of Diffusion MR Tractography" <doi:10.1016/C2018-0-02520-7> for more about the mathematical and computational underpinnings of diffusion MRI tractography.

Get spatial vector data from the Atlas du Patrimoine (<http://atlas.patrimoines.culture.fr/atlas/trunk/>), the official national platform of the French Ministry of Culture, and facilitate its use within R geospatial workflows. The package provides functions to list available heritage datasets, query and retrieve heritage data using spatial queries based on user-provided sf objects, perform spatial filtering operations, and return results as sf objects suitable for spatial analysis, mapping, and integration into heritage management and landscape studies.

Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.

This package provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) <doi:10.1038/s41586-018-0273-1>.

This package provides the function fancycut() which is like cut() except you can mix left open and right open intervals with point values, intervals that are closed on both ends and intervals that are open on both ends.

Create a flip over style Flash Card with desired data frame for Shiny application.

The free group in R; juxtaposition is represented by a plus. Includes inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms. To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2212.05883>.

Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations). For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

Allows the user to create a countdown in RMarkdown documents and shiny applications. The package is a wrapper of the JavaScript library flipdown.js'. See <https://pbutcher.uk/flipdown/> for more info.

Compute maximum likelihood estimators of parameters in a Gaussian factor model using the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>. In contrast to the factanal() function from stats package, fad() can handle high-dimensional datasets where number of variables exceed the sample size and is also substantially faster than the EM algorithms.

Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.

This package provides a dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos (http://pages.bloomsbury.com/favouritenumber).

Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the mgcv'-package.

This package provides high-performance online changepoint detection in univariate and multivariate data streams. Implements efficient C++ backends for the focus', md-focus and np-focus algorithms, with an R interface for real-time monitoring and offline analysis. The package bundles code from Qhull <http://www.qhull.org/>, by C. B. Barber and The Geometry Center. See inst/COPYRIGHTS for details.

Statistical methods and simulation tools for the interpretation of forensic DNA mixtures. The methods implemented are described in Haned et al. (2011) <doi:10.1111/j.1556-4029.2010.01550.x>, Haned et al. (2012) <doi:10.1016/j.fsigen.2012.11.002> and Gill & Haned (2013) <doi:10.1016/j.fsigen.2012.08.008>.

This package provides a set of methods to simulate from and fit computational models of attentional selectivity. The package implements the dual-stage two-phase (DSTP) model of Hübner et al. (2010) <doi:10.1037/a0019471>, and the shrinking spotlight (SSP) model of White et al. (2011) <doi:10.1016/j.cogpsych.2011.08.001>.

Finds the URL to the favicon for a website. This is useful if you want to display the favicon in an HTML document or web application, especially if the website is behind a firewall.