This package provides an interface to the Flickr API <https://www.flickr.com/services/api/> and allows R users to download data on Flickr.

Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ...

This package provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package plm by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.

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>.

Create and visualize fractal trees and fractal forests, based on the Lindenmayer system (L-system). For more details see Lindenmayer (1968a) <doi:10.1016/0022-5193(68)90079-9> and Lindenmayer (1968b) <doi:10.1016/0022-5193(68)90080-5>.

This package provides a joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal biomarkers are modelled using a linear mixed effects model. The association between the longitudinal submodel and the survival submodel is captured through shared random effects. It allows researchers to analyze large-scale data to model biomarker trajectories, estimate their effects on event outcomes, and dynamically predict future events from patientsâ past histories. A function for simulating survival and longitudinal data for multiple biomarkers is also included alongside built-in datasets.

YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. stale_package_check() tries to identify any library() calls to unused packages.

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

Data from various catalogs of astrophysical gamma-ray sources detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071, 2009 June 1), on board the Fermi gamma-ray satellite. More information on Fermi and its data products is available from the Fermi Science Support Center (http://fermi.gsfc.nasa.gov/ssc/).

Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.

This package provides a population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.

Curry, Compose, and other higher-order functions.

This package provides functions for fitting data to a quiescent growth model, i.e. a growth process that involves members of the population who stop dividing or propagating.

For each feature, a score is computed that can be useful for feature selection. Several random subsets are sampled from the input data and for each random subset, various linear models are fitted using lars method. A score is assigned to each feature based on the tendency of LASSO in including that feature in the models.Finally, the average score and the models are returned as the output. The features with relatively low scores are recommended to be ignored because they can lead to overfitting of the model to the training data. Moreover, for each random subset, the best set of features in terms of global error is returned. They are useful for applying Bolasso, the alternative feature selection method that recommends the intersection of features subsets.

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.

This package provides a collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

This package contains four main functions (i.e., four pieces of furniture): table1() which produces a well-formatted table of descriptive statistics common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.

Linear cross-section factor model fitting with least-squares and robust fitting the lmrobdetMM() function from RobStatTM'; related volatility, Value at Risk and Expected Shortfall risk and performance attribution (factor-contributed vs idiosyncratic returns); tabular displays of risk and performance reports; factor model Monte Carlo. The package authors would like to thank Chicago Research on Security Prices,LLC for the cross-section of about 300 CRSP stocks data (in the data.table object stocksCRSP', and S&P GLOBAL MARKET INTELLIGENCE for contributing 14 factor scores (a.k.a "alpha factors".and "factor exposures") fundamental data on the 300 companies in the data.table object factorSPGMI'. The stocksCRSP and factorsSPGMI data are not covered by the GPL-2 license, are not provided as open source of any kind, and they are not to be redistributed in any form.

This package provides a financial calculator that provides very fast implementations of common financial indicators using Rust code. It includes functions for bond-related indicators, such as yield to maturity ('YTM'), modified duration, and Macaulay duration, as well as functions for calculating time-weighted and money-weighted rates of return (using Modified Dietz method) for multiple portfolios, given their market values and profit and loss ('PnL') data. fcl is designed to be efficient and accurate for financial analysis and computation. The methods used in this package are based on the following references: <https://en.wikipedia.org/wiki/Modified_Dietz_method>, <https://en.wikipedia.org/wiki/Time-weighted_return>.

Fast reverse complement of DNA and RNA sequences using a C++ lookup table for O(1) per-base complement mapping. Supports full IUPAC ambiguity codes, DNA and RNA modes, case preservation, and NA handling. Much faster than other packages for computing reverse complements of many short sequences such as primers, probes, and, k-mers.

This package creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the fixest package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.

Access and retrieve vocabulary data Finto API <https://api.finto.fi/>, which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas.

Include a countdown <https://github.com/PButcher/flipdown> in all R contexts with the convenience of htmlwidgets'.

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>.