Unofficial API wrapper for Euroleague and Eurocup basketball API (<https://www.euroleaguebasketball.net/en/euroleague/>), it allows to retrieve real-time and historical standard and advanced statistics about competitions, teams, players and games.

This package implements three complementary pipelines for causal analysis on macroeconomic time series: (1) Error-Correction Models with Multivariate Adaptive Regression Splines (ECM-MARS), (2) Bayesian Structural Time Series (BSTS), and (3) Bayesian GLM with AR(1) errors validated with Leave-Future-Out (LFO). Heavy backends (Stan) are optional and never used in examples or tests.

This package provides functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results.

Providing easy, portable access to NASA EarthData products through the use of bearer tokens. Much of NASA's public data catalogs hosted and maintained by its 12 Distributed Active Archive Centers ('DAACs') are now made available on the Amazon Web Services S3 storage. However, accessing this data through the standard S3 API is restricted to only to compute resources running inside us-west-2 Data Center in Portland, Oregon, which allows NASA to avoid being charged data egress rates. This package provides public access to the data from any networked device by using the EarthData login application programming interface (API), <https://www.earthdata.nasa.gov/data/earthdata-login>, providing convenient authentication and access to cloud-hosted NASA EarthData products. This makes access to a wide range of earth observation data from any location straight forward and compatible with R packages that are widely used with cloud native earth observation data (such as terra', sf', etc.).

Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.

Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics <doi:10.1080/10618600.2025.2535600> and arXiv <doi:10.48550/arXiv.2404.01043>. Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway <doi:10.13140/RG.2.2.34500.23685>. Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC.

This package provides a collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) <doi:10.3109/1354750X.2010.533285>); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) <doi:10.1289/EHP5838>).

Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty.

This package provides a system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs <https://emitanaka.org/edibble-book/>. An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow.

The purpose of this package is to support the setup the R environment. The two main features are autos', to automatically source files and/or directories into your environment, and paths to consistently set path objects across projects for input and output. Both are implemented using a configuration file to allow easy, custom configurations that can be used for multiple or all projects.

In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.

Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: HTML', LaTeX', and Excel'.

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths, represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They are markedly superior to Venn diagrams for this purpose, especially in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. As data analysis tools, eikosograms display categorical data in a manner similar to Mosaic plots, especially when only two variates are involved (the only case in which they are essentially identical, though eikosograms purposely disallow spacing between rectangles). Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate (beyond two) is introduced. Instead, only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model) than is a Mosaic plot. Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis. Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn appearing as the response. This makes it better suited than Mosaic plots to discrete graphical models based on conditional independence graphs (i.e. "Bayesian Networks" or "BayesNets"). The eikosogram and its superiority to Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.

Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EMâ s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.

Extra strength glue for data-driven templates. String interpolation for Shiny apps or R Markdown and knitr'-powered Quarto documents, built on the glue and whisker packages.

The epilogi variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics, 39(9): btad545. <doi:10.1093/bioinformatics/btad545>.

The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayesâ rule.

Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.

Gene information from Ensembl genome builds GRCh38.p14 and GRCh37.p13 to use with the topr package. The datasets were originally downloaded from <https://ftp.ensembl.org/pub/current/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz> and <https://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz> and converted into the format required by the topr package. See <https://github.com/totajuliusd/topr?tab=readme-ov-file#how-to-use-topr-with-other-species-than-human> to see the required format.

Collection of functions to evaluate uncertainty of results from water quality analysis using the Weighted Regressions on Time Discharge and Season (WRTDS) method. This package is an add-on to the EGRET package that performs the WRTDS analysis. The WRTDS modeling method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>, and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The paper describing the uncertainty and confidence interval calculations is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.

This is a collection of assorted functions and examples collected from various projects. Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using grf'.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>, provides a detailed introduction to the package.

This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <https://www.vanderwalresearch.com/blog/15-elections>.

Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations.