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.

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

This package provides functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.

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>.

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: <doi:10.32942/osf.io/dg7hw>).

This package provides fast alternatives to standard survival analysis functions in the survival package, together with tools for time-to-event trial simulation and sequential analysis. The estimation and testing functions cover a single-time-point Kaplan-Meier estimator (survfit_fast()), log-rank tests including weighted and stratified variants (survdiff_fast()), a closed-form hazard ratio estimator based on the Pike-Halley Estimator method (coxph_fast()), restricted mean survival time (rmst_fast()), window mean survival time (wmst_fast()), milestone survival comparison (milestone_fast()), median survival time (medsurv_fast()), the max-combo test (maxcombo_fast()), the robust modestly-weighted log-rank test (rmw_fast()), the weighted Kaplan-Meier (Pepe-Fleming) test (wkm_fast()), the average hazard with survival weight (ahsw_fast()), and the Kalbfleisch-Prentice average hazard ratio (ahr_fast()). The simulation layer generates individual patient data (simdata_fast()), performs interim or sequential analyses (analysis_fast()), and aggregates operating characteristics (simsummary_fast()). A visualization layer assembles design-stage scenarios (gen_scenario_fast()) and builds analysis-stage Kaplan-Meier curves (kmcurve_fast()), each with plot and print methods. All functions are designed for repeated evaluation inside large simulation loops, such as adaptive sample-size re-estimation, probability-of-success calculations, and regional consistency evaluation in multi-regional trials. Core computations are implemented in C++ via Rcpp for maximum performance. Methodological background is described in Collett (2014, ISBN:9780429196294).

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

This package provides functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

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.

This package provides a data package that hosts all models for the nflfastR package.

In the Cramérâ Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. Currently, there are three methods implemented: The first one uses saddlepoint approximation (two variants are provided), the second one uses importance sampling and the third one is based on the simulation of a dual process. This last method is not very accurate and only given here for completeness.

The function estimates a multivariate regression model for outcomes with network dependence.

Eigenvalue-based estimation of the number of factors in approximate factor models. Designed to work when either N or T is large, without requiring both dimensions to grow simultaneously. Implements the eigenvalue ratio estimator of Ahn and Horenstein (2013) <doi:10.3982/ECTA8968>, the information criteria of Bai and Ng (2002) <doi:10.1111/1468-0262.00273>, the tuned penalty of Alessi, Barigozzi and Capasso (2010) <doi:10.1016/j.spl.2010.08.005>, the auto-covariance ratio estimator of Lam and Yao (2012) <doi:10.1214/12-AOS970>, and the edge distribution estimators of Onatski (2009) <doi:10.3982/ECTA6964> and Onatski (2010) <doi:10.1162/REST_a_00043>.

Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.

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.

Satellite data collected between 2003 and 2022, in conjunction with gridded bathymetric data (50-150 m resolution), are used to estimate the irradiance reaching the bottom of a series of representative EU Arctic fjords. An Earth System Science Data (ESSD) manuscript, Schlegel et al. (2024), provides a detailed explanation of the methodology.

Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to constrained ordination. The package contains plotting and summary functions as well as the analyses.

This package implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records <doi:10.1017/S0003055418000783> and is available at <https://imai.fas.harvard.edu/research/linkage.html>.

This package provides a flexible framework for estimating factor models with multiple latent variables. Supports linear, probit, ordered probit, and multinomial logit model components. Features include multi-stage estimation, automatic parameter initialization, analytical gradients and Hessians, and parallel estimation. Methods are described in Heckman, Humphries, and Veramendi (2016) <doi:10.1016/j.jeconom.2015.12.001>, Heckman, Humphries, and Veramendi (2018) <doi:10.1086/698760>, and Humphries, Joensen, and Veramendi (2024) <doi:10.1257/pandp.20241026>.

This package provides tools for evaluating the incremental economic consequences of a proposed farm-management change using partial-budget logic. Functions organize added returns, reduced costs, added costs, and reduced returns; compare baseline and alternative budgets; calculate net changes and marginal rates of return; conduct one- and two-way sensitivity, scenario, break-even, dominance, marginal, and Monte Carlo uncertainty analyses; and convert capital changes to annual equivalents. The framework follows the approach described by the International Maize and Wheat Improvement Center (1988, ISBN: 968-6127-19-4) for farm-management, extension, and on-farm research.

Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values.

Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.

Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.

This package provides tools to estimate the genome size of polyploid species using k-mer frequencies. This package includes functions to process k-mer frequency data and perform genome size estimation by fitting k-mer frequencies with a normal distribution model. It supports handling of complex polyploid genomes and offers various options for customizing the estimation process. The basic method findGSE is detailed in Sun, Hequan, et al. (2018) <doi:10.1093/bioinformatics/btx637>.