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>.

Open-source package for computing likelihood ratios in kinship testing and human identification cases. It has the core function of the software GENis, developed by Fundación Sadosky. It relies on a Bayesian Networks framework and is particularly well suited to efficiently perform large-size queries against databases of missing individuals.

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>.

Enhances the functionality of the mvbutils::foodweb() program. The matrix-format output of the original program contains identical row names and column names, each name representing a retrieved function. This format is enhanced by using the find_funs() program [see Sebastian (2017) <https://sebastiansauer.github.io/finds_funs/>] to concatenate the package name to the function name. Each package is assigned a unique color, that is used to color code the text naming the packages and the functions. This color coding is extended to the entries of value "1" within the matrix, indicating the pattern of ancestor and descendent functions.

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.

Simplifies the creation and customization of forest plots (alternatively called dot-and-whisker plots). Input classes accepted by forplo are data.frame, matrix, lm, glm, and coxph. forplo was written in base R and does not depend on other packages.

Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340.

Obtain Formula 1 data via the Jolpica API <https://jolpi.ca> and the unofficial API <https://www.formula1.com/en/timing/f1-live> via the fastf1 Python library <https://docs.fastf1.dev/>.

Several functions to compute indicators for organization and efficiency in visual foraging, multi-target visual search, and cancellation tasks. The current version of this package includes the following indicators: best-r, mean Inter-target Distance, Percentage Above Optimal (PAO) scan path, and intersections in the scan path. For more detailed descriptions, see Mark et al. (2004) <doi:10.1212/01.WNL.0000131947.08670.D4>.

Fuzzy string matching implementation of the fuzzywuzzy <https://github.com/seatgeek/fuzzywuzzy> python package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.

Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a shiny app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.

Extends the capabilities for flexible partitioning and model-based clustering available in the packages flexclust and flexmix to handle ordinal and mixed-with-ordinal data types via new distance, centroid and driver functions that make various assumptions regarding ordinality. Using them within the flex-scheme allows for easy comparisons across methods.

Climate is a critical component limiting growing range of plant species, which also determines cultivar adaptation to a region. The evaluation of climate influence on fruit production is critical for decision-making in the design stage of orchards and vineyards and in the evaluation of the potential consequences of future climate. Bio- climatic indices and plant phenology are commonly used to describe the suitability of climate for growing quality fruit and to provide temporal and spatial information about regarding ongoing and future changes. fruclimadapt streamlines the assessment of climate adaptation and the identification of potential risks for grapevines and fruit trees. Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) <doi:10.5194/gmd-9-2315-2016>), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) <https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>), iii) estimate plant phenology (Schwartz (2012) <doi:10.1007/978-94-007-6925-0>), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) <doi:10.3354/cr01532>), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).

Downloads all the datasets (you can exclude the daily ones or specify a list of those you are targeting specifically) from Kenneth French's Website at <https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, process them and convert them to list of xts (time series).

Create secure, encrypted, and password-protected static HTML documents that include the machinery for secure in-browser authentication and decryption.

This package implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.

Calculates the fused extended two-way fixed effects (FETWFE) estimator for unbiased and efficient estimation of difference-in-differences in panel data with staggered treatment adoption. This estimator eliminates bias inherent in conventional two-way fixed effects estimators, while also employing a novel bridge regression regularization approach to improve efficiency and yield valid standard errors. Also implements extended TWFE (etwfe) and bridge-penalized ETWFE (betwfe). Provides S3 classes for streamlined workflow and supports flexible tuning (ridge and rank-condition guarantees), automatic covariate centering/scaling, and detailed overall and cohort-specific effect estimates with valid standard errors. Includes simulation and formatting utilities, extensive diagnostic tools, vignettes, and examples. See Faletto (2025) (<doi:10.48550/arXiv.2312.05985>).

Robust analysis using forward search in linear and generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to easily import, filter, and modify ForestGEO data. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.

Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.

Automatically perform a reanalysis series on a data set using CNA, and calculate the fit-robustness of the resulting models, as described in Parkkinen and Baumgartner (2021) <doi:10.1177/0049124120986200>.

Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers. The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

This package provides a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.