Analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments using nonlinear mixed-effects regression models and analysis of the results. FRApp is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) <doi:10.1038/s41598-025-87154-w>.

This package provides a set of tools for data wrangling, spatial data analysis, statistical modeling (including direct, model-assisted, photo-based, and small area tools), and USDA Forest Service data base tools. These tools are aimed to help Foresters, Analysts, and Scientists extract and perform analyses on USDA Forest Service data.

The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>. The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains a class of functions, CausalANOVA, which estimates the average marginal interaction effects (AMIEs) by a regularized ANOVA as proposed by Egami and Imai (2019). It contains a variety of regularization techniques to facilitate analysis of large factorial experiments.

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.

This package provides tools to support sensible statistics for functional response analysis.

Uses three different correlation coefficients to calculate measurement-level adequate correlations in a feature matrix: Pearson product-moment correlation coefficient, Intraclass correlation and Cramer's V.

Fatty acid metabolic analysis aimed to the estimation of FA import (I), de novo synthesis (S), fractional contribution of the 13C-tracers (D0, D1, D2), elongation (E) and desaturation (Des) based on mass isotopologue data.

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.

Computes relative importance of main and interaction effects. Also, sum of the modified generalized weights is computed. Ibrahim et al. (2022) <doi:10.1134/S1064229322080051>.

This package provides a drop-in replacement for flexdashboard Rmd documents, which implements an after-knit-hook to split the generated single page application in one document per main section to reduce rendering load in the web browser displaying the document. Put all JavaScript stuff needed in all sections before the first headline featuring navigation menu attributes. This package is experimental and maybe replaced by a solution inside flexdashboard'.

This package provides methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics, forthcoming. Preprint <arXiv:1611.04460>.

This data contains a large variety of information on players and their current attributes on Fantasy Premier League <https://fantasy.premierleague.com/>. In particular, it contains a `next_gw_points` (next gameweek points) value for each player given their attributes in the current week. Rows represent player-gameweeks, i.e. for each player there is a row for each gameweek. This makes the data suitable for modelling a player's next gameweek points, given attributes such as form, total points, and cost at the current gameweek. This data can therefore be used to create Fantasy Premier League bots that may use a machine learning algorithm and a linear programming solver (for example) to return the best possible transfers and team to pick for each gameweek, thereby fully automating the decision making process in Fantasy Premier League. This function simply supplies the required data for such a task.

This package implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The fastTopics package is a successor to the CountClust package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.

Fast, numerically robust computation of weighted moments via Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.

An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm themselves.

"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.".

Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.

Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) <doi:10.1214/aos/1013699993>, and Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>. These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations.

Several generalized / directional Fixed Sequence Multiple Testing Procedures (FSMTPs) are developed for testing a sequence of pre-ordered hypotheses while controlling the FWER, FDR and Directional Error (mdFWER). All three FWER controlling generalized FSMTPs are designed under arbitrary dependence, which allow any number of acceptances. Two FDR controlling generalized FSMTPs are respectively designed under arbitrary dependence and independence, which allow more but a given number of acceptances. Two mdFWER controlling directional FSMTPs are respectively designed under arbitrary dependence and independence, which can also make directional decisions based on the signs of the test statistics. The main functions for each proposed generalized / directional FSMTPs are designed to calculate adjusted p-values and critical values, respectively. For users convenience, the functions also provide the output option for printing decision rules.

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 templates for reports in rmarkdown and functions to create tables and summaries of data.

Some basic procedures for dealing with log maximally skew stable distributions, which are also called finite moment log stable distributions.

This package contains the methods proposed by Geyer and Meeden (2005)<doi:10.1214/088342305000000340> and Trigo et al. (2025) <doi:10.47749/T/UNICAMP.2025.1500297> to construct fuzzy confidence intervals. Compute and plot the fuzzy membership functions of the methods, and the expected length compared with the infimum.

Create a flip over style Flash Card with desired data frame for Shiny application.