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'.

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.

Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.

Create a forest plot based on the layout of the data. Confidence intervals in multiple columns by groups can be done easily. Editing the plot, inserting/adding text, applying a theme to the plot, and much more.

For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available.

Automatically perform a reanalysis series on a data set using CNA, and calculate the fit-robustness of the resulting models, as described in Parkkinen and Baumgartner (2021) <doi:10.1177/0049124120986200>.

This package creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the RStudio viewer pane, included in a R Markdown document or in a Shiny application. Also provides a Shiny application allowing to run this widget and to navigate in the files found by the search. Instead of creating a HTML widget, it is also possible to get the results of the search in a tibble'. The search is performed by the grep command-line utility.

This package provides a reproducible, map-oriented workflow for flood hazard assessment that chains rainfall extreme value analysis, rainfall-runoff simulation, terrain-based flow routing and water-depth estimation into a single pipeline. A stationary-versus-nonstationary test for changing rainfall extremes is built in, and any flood scenario can be produced for a present-day or a climate-adjusted design event. Defaults target settings with sparse gauge networks, using satellite or reanalysis rainfall, temperature-based potential evapotranspiration and regional pooling of short records. Heavy modelling engines are wrapped rather than reimplemented so that the core stays lightweight. Methods follow established hydrology, including the generalized extreme value distribution for rainfall maxima (Coles, 2001, <doi:10.1007/978-1-4471-3675-0>) and Manning's equation for open-channel flow.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers.

Fits probability distributions to data and plugs into the probaverse suite of R packages so distribution objects are ready for further manipulation and evaluation. Supports methods such as maximum likelihood and L-moments, and provides diagnostics including empirical ranking and quantile score.

Frequentist assisted by Bayes (FAB) confidence interval construction. See Adaptive multigroup confidence intervals with constant coverage by Yu and Hoff <DOI:10.1093/biomet/asy009> and Exact adaptive confidence intervals for linear regression coefficients by Hoff and Yu <DOI:10.1214/18-EJS1517>.

Computes unidimensional and multidimensional Reciprocity and Inaccuracy indices. These indices are applicable to common heterostylous populations and to any other type of stylar dimorphic and trimorphic populations, such as in enantiostylous and three-dimensional heterostylous plants. Simón-Porcar, V., A. J. Muñoz-Pajares, J. Arroyo, and S. D. Johnson. (in press) "FlowerMate: multidimensional reciprocity and inaccuracy indices for style-polymorphic plant populations.".

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.

Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.

Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.

This package provides a comprehensive set of datasets and tools for causal inference research. The package includes data from clinical trials, cancer studies, epidemiological surveys, environmental exposures, and health-related observational studies. Designed to facilitate causal analysis, risk assessment, and advanced statistical modeling, it leverages datasets from packages such as causalOT', survival', causalPAF', evident', melt', and sanon'. The package is inspired by the foundational work of Pearl (2009) <doi:10.1017/CBO9780511803161> on causal inference frameworks.

Small set of functions designed to speed up the computation of certain matrix operations that are commonly used in statistics and econometrics. It provides efficient implementations for the computation of several structured matrices, matrix decompositions and statistical procedures, many of which have minimal memory overhead. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.

Visualize as flow diagrams the logic of functions, expressions or scripts in a static way or when running a call, visualize the dependencies between functions or between modules in a shiny app, and more.

This package provides interface to the MATLAB toolbox Flexible Statistical Data Analysis (FSDA) which is comprehensive and computationally efficient software package for robust statistics in regression, multivariate and categorical data analysis. The current R version implements tools for regression: (forward search, S- and MM-estimation, least trimmed squares (LTS) and least median of squares (LMS)), for multivariate analysis (forward search, S- and MM-estimation), for cluster analysis and cluster-wise regression. The distinctive feature of our package is the possibility of monitoring the statistics of interest as a function of breakdown point, efficiency or subset size, depending on the estimator. This is accompanied by a rich set of graphical features, such as dynamic brushing, linking, particularly useful for exploratory data analysis.

This package provides a very flexible framework for building server side logic in R. The framework is unopinionated when it comes to how HTTP requests and WebSocket messages are handled and supports all levels of app complexity; from serving static content to full-blown dynamic web-apps. Fiery does not hold your hand as much as e.g. the shiny package does, but instead sets you free to create your web app the way you want.

Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.