This package provides a flexible interface to the Financial Modeling Prep API <https://site.financialmodelingprep.com/developer/docs>. The package supports all available endpoints and parameters, enabling R users to interact with a wide range of financial data.

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

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>.

Formula 1 pit stop data. The package provides information on teams and drivers across seasons (2025 or higher). It also includes a function to visualize pit stop performance.

This package provides methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses disordR discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) <doi:10.48550/arXiv.2307.13184>.

This contains functions that can be used to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve for correlated right-censored time-to-event data. See Beyene and Chen (2024) <doi:10.1177/09622802231220496>.

New and faster implementations for quantile quantile plots. The package also includes a function to prune data for quantile quantile plots. This can drastically reduce the running time for large samples, for 100 million samples, you can expect a factor 80X speedup.

An R client for the freecurrencyapi.com currency conversion API. The API requires registration of an API key. You can find the full API documentation at <https://freecurrencyapi.com/docs> .

This package provides a utility to scrape and load play-by-play data and statistics from the Premier Hockey Federation (PHF) <https://www.premierhockeyfederation.com/>, formerly known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API <https://www.nhl.com/>.

This package provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>, Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>, Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>, Centofanti et al. (2025) <doi:10.1080/00224065.2024.2430978>, Capezza et al. (2025) <doi:10.48550/arXiv.2410.20138>. The package is thoroughly illustrated in the paper of Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.

Some basic procedures for dealing with log maximally skew stable distributions, which are also called finite moment log stable distributions.

Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.

This package implements numerical entropy-pooling for portfolio construction and scenario analysis as described in Meucci, Attilio (2008) and Meucci, Attilio (2010) <doi:10.2139/ssrn.1696802>.

Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.

Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.

This package provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. fddm implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in C++ providing faster speed than existing packages.

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.

This package provides a wrapper for the API of the Danish Parliament. It makes it possible to get data from the API easily into a data frame. Learn more at <http://www.ft.dk/dokumenter/aabne_data>.

This package provides a C++ API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates Rcpp programming, enabling the development of R'-like code in C++ where functions can be defined on the fly and use variables in the surrounding environment.

Package for time value of money calculation, time series analysis and computational finance.

This package provides a collection of functions which fit functional neural network models. In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights.

Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package fuel implements thirty-nine existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.

Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.

This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.