Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

Converts R data frames and sf spatial objects into JSON and GeoJSON strings. The core encoders are implemented in Rust using the extendr framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized JSON text, allowing applications such as shiny or web APIs to transfer data to client-side JavaScript libraries without additional encoding overhead.

Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.

This package provides a program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.

Forest data quality is a package containing nine methods of analysis for forest databases, from databases containing inventory data and growth models, the focus of the analyzes is related to the quality of the data present in the database with a focus on consistency , punctuality and completeness of data.

This package provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the tscount package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the glarma package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood.

Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the seqDesign package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.

Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.

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.

This package provides a fast method for approximating time-varying infectious disease transmission rates from disease incidence time series and other data, based on a discrete time approximation of an SEIR model, as analyzed in Jagan et al. (2020) <doi:10.1371/journal.pcbi.1008124>.

An implementation of the methodology described in Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the capsushe package.

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

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>).

"This package quantifies the provenance of sediments in a catchment or study area. Based on a characterization of the sediment sources and the end sediment mixtures, a mixing model algorithm is applied to the sediment mixtures to estimate the relative contribution of each potential source. The package includes several graphs to help users in their data understanding, such as box plots, correlation, PCA, and LDA graphs. In addition, new developments such as the Consensus Ranking (CR), Consistent Tracer Selection (CTS), and Linear Variability Propagation (LVP) methods are included to correctly apply the fingerprinting technique and increase dataset and model understanding. A new method based on Conservative Balance (CB) method has also been included to enable the use of isotopic tracers.".

The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June of the following calendar year. As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity.

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.

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.

This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals.

Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See <https://lrberge.github.io/fplot/> for more information.

Collect your data on digital marketing campaigns from Facebook Leads Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

An R client for the "fixer.io" currency conversion and exchange rate API. The API requires registration and some features are only available on paid accounts. The full API documentation is available at <https://fixer.io/documentation>.

Given a multivariate dataset and some knowledge about the dependencies between its features, it is customary to fit a statistical model to the features to infer parameters of interest. Such a procedure implicitly assumes that the sample is exchangeable. This package provides a flexible non-parametric test of this exchangeability assumption, allowing the user to specify the feature dependencies by hand as long as features can be grouped into disjoint independent sets. This package also allows users to test a dual hypothesis, which is, given that the sample is exchangeable, does a proposed grouping of the features into disjoint sets also produce statistically independent sets of features? See Aw, Spence and Song (2023) for the accompanying paper.