Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.

Allows ATA (Automatic Time series analysis using the Ata method) models from the ATAforecasting package to be used in a tidy workflow with the modeling interface of fabletools'. This extends ATAforecasting to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).

Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elà as, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elà as, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elà as, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.

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>.

Generates RProtobuf classes for FactSet STACH V2 tabular format which represents complex multi-dimensional array of data. These classes help in the serialization and deserialization of STACH V2 formatted data. See GitHub repository documentation for more information.

This package provides functions to switch the BLAS'/'LAPACK optimized backend and change the number of threads without leaving the R session, which needs to be linked against the FlexiBLAS wrapper library <https://www.mpi-magdeburg.mpg.de/projects/flexiblas>.

An interactive shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.

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.

This package provides a study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the median survival time) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. Though The survival endpoint is often encountered in phase II trials, the existing SSD methods cannot deal with the survival endpoint. Furthermore, the existing SSD wonâ t control the type I error rate. The proposed designs can â partiallyâ control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

This package provides functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.

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>).

This package provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using ConsensusPathDB from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale.

Anonymized data from surveys conducted by Forwards <https://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <https://www.r-project.org/useR-2016/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.

Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. Also generates functional network data from three models, and evaluates a network generalized cross-validation criterion for dimension selection. For more information, see MacDonald, Zhu and Levina (2022+) <arXiv:2210.07491>.

This package provides a neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.

This package provides a well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.

The tools herein calculate, print, summarize and plot pairwise differences that result from generalized linear models, general linear hypothesis tests and multinomial logistic regression models. For more information, see Armstrong (2013) <doi:10.32614/RJ-2013-021>.

Enhance R help system by fuzzy search and preview interface, pseudo-postfix operators, and more. The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).

Calculate numerical asymptotic distribution functions of likelihood ratio statistics for fractional unit root tests and tests of cointegration rank. For these distributions, the included functions calculate critical values and P-values used in unit root tests, cointegration tests, and rank tests in the Fractionally Cointegrated Vector Autoregression (FCVAR) model. The functions implement procedures for tests described in the following articles: Johansen, S. and M. Ã . Nielsen (2012) <doi:10.3982/ECTA9299>, MacKinnon, J. G. and M. Ã . Nielsen (2014) <doi:10.1002/jae.2295>.

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

Miscellaneous utilities, tools and helper functions for finding and searching files on disk, searching for and removing R objects from the workspace. Does not import or depend on any third party package, but on core R only (i.e. it may depend on packages with priority base').

This package contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.

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/>).