Use R to access to the FMP Cloud API <https://fmpcloud.io/> and Financial Modeling Prep API <https://financialmodelingprep.com/developer/docs/>. Data available includes stock prices, market indexes, company fundamentals, 13F holdings data, and much more. A valid API token must be set to enable functions.

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.

This package provides a collection of fortunes from the R community.

The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions fractions and rational of the MASS package, but using a radically improved programming strategy.

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) <doi:10.48550/arXiv.1904.10265>. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly shiny application is included.

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 contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society Financial Mathematics exam, and some topics in the Models for Financial Economics exam.

This package provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) <doi:10.1214/009053605000000660>. Sentürk, D., Müller, H.G. (2010) <doi:10.1198/jasa.2010.tm09228>.

Generate SPSS'/'SAS styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.

Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.

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.

This package provides functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), Flight program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are hundreds of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>, and Pennycuick (2008, ISBN:9780080557816).

This package implements instrumental variable estimators for 2^K factorial experiments with noncompliance.

The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed. What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process. Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) <doi:10.1007/s40595-018-0107-y>.

Constructs and visualises trade-off functions for f-differential privacy (f-DP) as introduced by Dong et al. (2022) <doi:10.1111/rssb.12454>. Supports Gaussian differential privacy, the f-DP generalisation of (epsilon, delta)-differential privacy, and accepts user-specified optimal type I / type II errors from which the lower convex hull trade-off function is automatically constructed.

This package provides a collection of functions for calculating Floristic Quality Assessment (FQA) metrics using regional FQA databases that have been approved or approved with reservations as ecological planning models by the U.S. Army Corps of Engineers (USACE). For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored in a sister R package, fqadata'. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Centerâ s Environmental Laboratory.

Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at <http://www.fitbit.com>.

We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) <doi:10.1016/j.physa.2024.130308>.

Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.

This package contains the methods proposed by Geyer and Meeden (2005)<doi:10.1214/088342305000000340> and Trigo et al. (2025) <doi:10.47749/T/UNICAMP.2025.1500297> to construct fuzzy confidence intervals. Compute and plot the fuzzy membership functions of the methods, and the expected length compared with the infimum.

This package contains functions to fetch data from various data sources. The user first creates a catalog of objects from a data source, then fetches data from the catalog. The package provides an easy way to access data from many different types of sources.

This package provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a JAX backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including CPU', CUDA', and METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.