Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/it/>) are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species identification is now based on EPPO species codes (<https://data.eppo.int/>).

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.

Tidy tools to apply filter-based supervised feature selection methods. These methods score and rank feature relevance using metrics such as p-values, correlation, and importance scores (Kuhn and Johnson (2019) <doi:10.1201/9781315108230>).

Implementation of dynamic principal component analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.

R implementations of standard financial engineering codes; vanilla option pricing models such as Black-Scholes, Bachelier, CEV, and SABR.

This package provides a Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics. The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population. The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) <doi:10.1214/20-BA1206> and Ascolani, Lijoi and Ruggiero (2023) <doi:10.3150/22-BEJ1504> that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past.

Fits functional generalized estimating equations for longitudinal functional outcomes and covariates using a one-step estimator that is fast even for large cluster sizes or large numbers of clusters. The package supports quasi-likelihoods derived from a range of families, common link functions, and several working correlation structures. Uncertainty quantification is based on sandwich variance estimators and bootstrap procedures that remain valid even when the working correlation is incorrectly specified. The package provides an implementation of the method described in Loewinger et al. (2025) <https://pmc.ncbi.nlm.nih.gov/articles/PMC12306803/>. For irregularly spaced AR(1) precision matrices, the package can optionally use the archived package irregulAR1'; if needed, it can be obtained from the CRAN Archive at <https://cran.r-project.org/src/contrib/Archive/irregulAR1/>.

Reads and writes ARFF files. ARFF (Attribute-Relation File Format) files are like CSV files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the WEKA machine learning Java toolbox. See <https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/> for further info on ARFF and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on WEKA'. farff gets rid of the Java dependency that RWeka enforces, and it is at least a faster reader (for bigger files). It uses readr as parser back-end for the data section of the ARFF file. Consistency with RWeka is tested on Github and Travis CI with hundreds of ARFF files from OpenML'.

An implementation of the Fish Bioenergetics 4.0 framework described in Deslauriers et al. (2017) <doi:10.1080/03632415.2017.1377558>. Provides automated parameter optimization, multi-prey diet modeling, and comprehensive energy budget simulations for fisheries research and aquaculture applications. An optional TMB (Template Model Builder) backend delivers 10-50x speedup in maximum likelihood estimation while maintaining full backward compatibility. Includes species-specific parameter databases and tools for modeling fish growth, consumption, and metabolism under varying environmental conditions.

Growth models and forest production require existing data manipulation and the creation of new data, structured from basic forest inventory data. The purpose of this package is provide functions to support these activities.

Routines for estimating tree fiber (tracheid) length distributions in the standing tree based on increment core samples. Two types of data can be used with the package, increment core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibres. The microscopy measured data consist of the observed lengths of the uncut fibres in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers (fines) that at least partially appear in the increment core, being the generalized gamma and the log normal densities.

Lints are code patterns that are not optimal because they are inefficient, forget corner cases, or are less readable. flir provides a small set of functions to detect those lints and automatically fix them. It builds on astgrepr', which itself uses the Rust crate ast-grep to parse and navigate R code.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

An implementation of the fair data adaptation with quantile preservation described in Plecko & Meinshausen (JMLR 2020, 21(242), 1-44). The adaptation procedure uses the specified causal graph to pre-process the given training and testing data in such a way to remove the bias caused by the protected attribute. The procedure uses tree ensembles for quantile regression. Instructions for using the methods are further elaborated in the corresponding JSS manuscript, see <doi:10.18637/jss.v110.i04>.

An easy framework to read FDA Adverse Event Reporting System XML/ASCII files.

This package provides a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

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 a simplified interface to the Central Data Repository REST API service made available by the United States Federal Financial Institutions Examination Council ('FFIEC'). Contains functions to retrieve reports of Condition and Income (Call Reports) and Uniform Bank Performance Reports ('UBPR') in list or tidy data frame format for most FDIC insured institutions. See <https://cdr.ffiec.gov/public/Files/SIS611_-_Retrieve_Public_Data_via_Web_Service.pdf> for the official REST API documentation published by the FFIEC'.

Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. When the model is executed a set of results are displayed which include efficiency scores, peers determination, scale efficiencies evaluation and slacks calculation. Fore more information about the theoretical background of the package, please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

Likelihood-free inference method for stochastic models. Uses a deterministic optimizer on simple simulations of the model that are performed with a prior drawn randomness by applying the inverse transform method. Is designed to work on its own and also by using the Julia package Jflimo available on the git page of the project: <https://metabarcoding.org/flimo>.

This package provides functions to access and retrieve metadata from the Finna API <https://api.finna.fi/>, which aggregates content from Finnish archives, libraries, and museums.

With the functions in this package you can check the validity of the following financial instrument identifiers: FIGI (Financial Instrument Global Identifier <https://www.openfigi.com/about/figi>), CUSIP (Committee on Uniform Security Identification Procedures <https://www.cusip.com/identifiers.html#/CUSIP>), ISIN (International Securities Identification Number <https://www.cusip.com/identifiers.html#/ISIN>), SEDOL (Stock Exchange Daily Official List <https://www2.lseg.com/SEDOL-masterfile-service-tech-guide-v8.6>). You can also calculate the FIGI checksum of 11-character strings, which can be useful if you want to create your own FIGI identifiers.

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