Screens daily streamflow time series for temporal trends and change-points. This package has been primarily developed for assessing the quality of daily streamflow time series. It also contains tools for plotting and calculating many different streamflow metrics. The package can be used to produce summary screening plots showing change-points and significant temporal trends for high flow, low flow, and/or baseflow statistics, or it can be used to perform more detailed hydrological time series analyses. The package was designed for screening daily streamflow time series from Water Survey Canada and the United States Geological Survey but will also work with streamflow time series from many other agencies. Package update to version 2.0 made updates to read.flows function to allow loading of GRDC and ROBIN streamflow record formats. This package uses the `changepoint` package for change point detection. For more information on change point methods, see the changepoint package at <https://cran.r-project.org/package=changepoint>.

Allows the user to implement easily canvas elements within a shiny app or an RMarkdown document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the fabricjs JavaScript library. See <http://fabricjs.com/>.

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.

Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace ImageJ (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.

This package provides functional tools such as fmap(), fwalk(), and fapply() to iterate over vectors, data frames, or grouped data with optional parallelism and real-time progress tracking. Designed for readable and reproducible workflows, including support for Monte Carlo simulations and benchmarking.

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>.

Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). FKF.SP was built upon the existing FKF package and is, in general, a faster Kalman filter/smoother.

This package provides a collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2> and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze bivariate and multivariate data sets of financial returns.

FASTQC is the most widely used tool for evaluating the quality of high throughput sequencing data. It produces, for each sample, an html report and a compressed file containing the raw data. If you have hundreds of samples, you are not going to open up each HTML page. You need some way of looking at these data in aggregate. fastqcr Provides helper functions to easily parse, aggregate and analyze FastQC reports for large numbers of samples. It provides a convenient solution for building a Multi-QC report, as well as, a one-sample report with result interpretations.

We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, <doi:10.18637/jss.v096.i07>.

R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.

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.

Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the mgcv'-package.

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.

The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model (see Friedman et al. (2010)), sparse linear discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.

Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.

This package performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's.

Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.

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>.

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce fibos', an R and Python package that extends the OS methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.

This package provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the fable framework provided by the fabletools package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Average rating and number of votes reported by IMDb for films and shorts with over 100 votes in 2022. The data are analysed in Chapter 3 of the Book Getting (more out of) Graphics (Antony Unwin, CRC Press 2024).

Processes data from The Social Networks and Fertility Survey, downloaded from <https://dataarchive.lissdata.nl>, including correcting respondent errors and transforming network data into network objects to facilitate analyses and visualisation.