Perform fuzzy joins on data frames using approximate string matching. Implements inner, left, right, full, semi, and anti joins with string distance metrics from the stringdist package, including Optimal String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram, cosine, Jaccard, and Soundex. Uses a data.table backend plus compiled C++ result assembly to reduce overhead in large joins, while adaptive candidate planning avoids unnecessary distance evaluations in single-column string joins. Suitable for reconciling misspellings, inconsistent labels, and other near-match identifiers while optionally returning the computed distance for each match.

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.

Forest data quality is a package containing nine methods of analysis for forest databases, from databases containing inventory data and growth models, the focus of the analyzes is related to the quality of the data present in the database with a focus on consistency , punctuality and completeness of data.

This package provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the fcm package returns a data frame of the concepts values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.

Allows prophet models from the prophet package to be used in a tidy workflow with the modelling interface of fabletools'. This extends prophet to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

This package provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.

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/>.

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented. FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given. Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.

Authenticate users in Shiny applications using Google Firebase with any of the many methods provided; email and password, email link, or using a third-party provider such as Github', Twitter', or Google'. Use Firebase Storage to store files securely, and leverage Firebase Analytics to easily log events and better understand your audience.

Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as rvest and RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, igraph', network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis.

It offers comprehensive tools for the analysis of functional time series data, focusing on white noise hypothesis testing and goodness-of-fit evaluations, alongside functions for simulating data and advanced visualization techniques, such as 3D rainbow plots. These methods are described in Kokoszka, Rice, and Shang (2017) <doi:10.1016/j.jmva.2017.08.004>, Yeh, Rice, and Dubin (2023) <doi:10.1214/23-EJS2112>, Kim, Kokoszka, and Rice (2023) <doi:10.1214/23-ss143>, and Rice, Wirjanto, and Zhao (2020) <doi:10.1111/jtsa.12532>.

This package contains the core functions associated with Fast Regularized Canonical Correlation Analysis. Please see the following for details: Raul Cruz-Cano, Mei-Ling Ting Lee, Fast regularized canonical correlation analysis, Computational Statistics & Data Analysis, Volume 70, 2014, Pages 88-100, ISSN 0167-9473 <doi:10.1016/j.csda.2013.09.020>.

Compute alpha and beta contributional diversity metrics, which is intended for linking taxonomic and functional microbiome data. See GitHub repository for the tutorial: <https://github.com/gavinmdouglas/FuncDiv/wiki>. Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) <doi:10.1093/bioinformatics/btac809>.

An implementation of methods presented by Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots for comparing institutional performance, for standardised ratios, ratios of counts and proportions with additive overdispersion adjustment.

The goal of this package is to provide an improved version of WA-PLS (Weighted Averaging Partial Least Squares) by including the tolerances of taxa and the frequency of the sampled climate variable. This package also provides a way of leave-out cross-validation that removes both the test site and sites that are both geographically close and climatically close for each cycle, to avoid the risk of pseudo-replication.

Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a Tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.

Create Frequently Asked Questions page for Shiny application.

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>. FFTs are simple and transparent decision trees for solving binary classification problems. FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.

Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). <doi:10.1007/978-3-031-27781-8_25>. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.

Probability distributions that are sometimes useful in hydrology.

This package provides tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.

An R client for the Federal Reserve Economic Data ('FRED') API <https://research.stlouisfed.org/docs/api/>. Functions to retrieve economic time series and other data from FRED'.

This package provides a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.