An easy framework to read FDA Adverse Event Reporting System XML/ASCII files <https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-latest-quarterly-data-files>.

This package implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.

This package provides functionality to plot airplane flight paths on maps. The plotted flight paths follow the great circle of the Earth.

Some functions of ade4 and stats are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. In order to permit different weights of the elements to be clustered, the function kmeansW', programmed in C++, is included. It is a modification of kmeans'. Some graphical functions include the option: gg=FALSE'. When gg=TRUE', they use the ggplot2 and ggrepel packages to avoid the super-position of the labels.

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

This package provides a collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>, MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>, total variation depth and modified shape similarity index by Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection tools and depths for functional data like functional boxplot, (modified) band depth etc., are also available.

High-performance tools for transport modeling - network processing, route enumeration, and traffic assignment in R. The package implements the Path-Sized Logit model for traffic assignment - Ben-Akiva and Bierlaire (1999) <doi:10.1007/978-1-4615-5203-1_2> - an efficient route enumeration algorithm, and provides powerful utility functions for (multimodal) network generation, consolidation/contraction, and/or simplification. The user is expected to provide a transport network (either a graph or collection of linestrings) and an origin-destination (OD) matrix of trade/traffic flows. Maintained by transport consultants at CPCS (cpcs.ca).

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) <doi:10.4310/SII.2013.v6.n2.a1>.

Helpers for parsing out the R functions and packages used in R scripts and notebooks.

Fast functions for timestamp manipulation that avoid system calls and take shortcuts to facilitate operations on very large data.

This package contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.

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

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>.

This package provides functionality for clustering origin-destination (OD) pairs, representing desire lines (or flows). This includes creating distance matrices between OD pairs and passing distance matrices to a clustering algorithm. See the academic paper Tao and Thill (2016) <doi:10.1111/gean.12100> for more details on spatial clustering of flows. See the paper on delineating demand-responsive operating areas by Mahfouz et al. (2025) <doi:10.1016/j.urbmob.2025.100135> for an example of how this package can be used to cluster flows for applied transportation research.

Climate is a critical component limiting growing range of plant species, which also determines cultivar adaptation to a region. The evaluation of climate influence on fruit production is critical for decision-making in the design stage of orchards and vineyards and in the evaluation of the potential consequences of future climate. Bio- climatic indices and plant phenology are commonly used to describe the suitability of climate for growing quality fruit and to provide temporal and spatial information about regarding ongoing and future changes. fruclimadapt streamlines the assessment of climate adaptation and the identification of potential risks for grapevines and fruit trees. Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) <https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>), iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).

This package provides methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

This package provides functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

Calculates the fused extended two-way fixed effects (FETWFE) estimator for unbiased and efficient estimation of difference-in-differences in panel data with staggered treatment adoption. This estimator eliminates bias inherent in conventional two-way fixed effects estimators, while also employing a novel bridge regression regularization approach to improve efficiency and yield valid standard errors. Also implements extended TWFE (etwfe) and bridge-penalized ETWFE (betwfe). Provides S3 classes for streamlined workflow and supports flexible tuning (ridge and rank-condition guarantees), automatic covariate centering/scaling, and detailed overall and cohort-specific effect estimates with valid standard errors. Includes simulation and formatting utilities, extensive diagnostic tools, vignettes, and examples. See Faletto (2025) (<doi:10.48550/arXiv.2312.05985>).

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>.

The proximate composition analysis is the quantification of main components that constitutes nutritional profile of any food and food products including fish, shellfish, fish feed and their ingredients. Understanding this composition is essential for evaluating their nutritional value and for making informed dietary choices. The primary components typically analyzed include; moisture/ water in foods, crude protein, crude fat/ lipid, total ash, fiber and carbohydrates AOAC(2005,ISBN:0-935584-77-3). In case of fish, shellfish and its products, the proximate composition consists of four primary constituents - water, protein, fat, and ash (mostly minerals). Fish exhibit significant variation in their chemical makeup based on age, sex, environment, and season, both within the same species and between individual fish. There is minimal fluctuation in the content of ash and protein. The lipid concentration varies remarkably and is inversely correlated with the water content. In case of fish, carbohydrates are present in minor quantity so that are quantified by subtracting total of other components from 100 to get percentage of carbohydrates.

Automated feature engineering functions tailored for credit scoring. It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment.

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

This package provides a set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.