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

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>.

An interface to the fastText library <https://github.com/facebookresearch/fastText>. The package can be used for text classification and to learn word vectors. An example how to use fastTextR can be found in the README file.

This package provides a small utility which wraps Rscript and provides access to all R functions from the shell.

This package provides optimized C++ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via OpenMP was implemented with assistance from the DeepSeek Artificial Intelligence Assistant (<https://www.deepseek.com/>).

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.

Generate SPSS'/'SAS styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.

Accompanying package of the book Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

An implementation in Rcpp / RcppArmadillo of Partial Least Square algorithms. This package includes other functions to perform the double cross-validation and a fast correlation.

Interface for accessing the Fortnite Ecosystem API, allowing users to retrieve island metadata and engagement metrics. The package provides functions to search for Fortnite Creative islands, retrieve detailed metadata about specific islands including titles, descriptions, and tags, and access engagement metrics such as daily active users and play duration. It supports pagination for large result sets and time-series analysis of island performance. The API endpoint is <https://api.fortnite.com/ecosystem/v1>.

Turn numeric,data.frame,matrix into fraction form.

Fresh biomass determination is the key to evaluating crop genotypes response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsuâ s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.

Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.

Aids in analysing data from a food frequency questionnaire known as the Harvard Service Food Frequency Questionnaire (HSFFQ). Functions from this package use answers from the HSFFQ to generate estimates of daily consumed micronutrients, calories, macronutrients on an individual level. The package also calculates food quotients on individual and group levels. Foodquotient calculation is an often tedious step in the calculation of total human energy expenditure (TEE) using the doubly labeled water method, which is the gold standard for measuring TEE.

This package provides a dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. The fpop package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints.

Datasets for teaching quantitative approaches and modeling in archaeology and paleontology. This package provides several types of data related to broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), which can be used to illustrate statistical methods in the classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.).

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>.

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms searches for locally optimal designs and EW optimal designs under the D-criterion. See Huang, Y., Li, K., Mandal, A., & Yang, J., (2024) <doi:10.1007/s11222-024-10465-x> and Lin, S., Huang, Y., & Yang, J. (2025) <doi:10.48550/arXiv.2505.00629>.

It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.

New and faster implementations for quantile quantile plots. The package also includes a function to prune data for quantile quantile plots. This can drastically reduce the running time for large samples, for 100 million samples, you can expect a factor 80X speedup.

Efficiently implementing two complementary methodologies for discovering motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023) "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data" <doi:10.1080/10618600.2022.2156522> is a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to identify recurring patterns (candidate functional motifs) across and within curves, allowing different portions of the same curve to belong to different clusters. It includes a family of distances and a normalization to discover various motif types and learns motif lengths in a data-driven manner. It can also be used for local clustering of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" <doi:10.48550/arXiv.2306.04254> applies hierarchical agglomerative clustering with a functional generalization of the Mean Squared Residue Score to identify motifs of a specified length in curves. This deterministic method includes a small set of user-tunable parameters. Both algorithms are suitable for single curves or sets of curves. The package also includes a flexible function to simulate functional data with embedded motifs, allowing users to generate benchmark datasets for validating and comparing motif discovery methods.

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>.

Multifactor nonparametric analysis of variance based on ranks. Builds on the Kruskal-Wallis H test and its 2x2 Scheirer-Ray-Hare extension to handle any factorial designs. Provides effect sizes, Dunn-Bonferroni pairwise-comparison matrices, and simple-effects analyses. Tailored for psychology and the social sciences, with beginner-friendly R syntax and outputs that can be dropped into journal reports. Includes helpers to export tab-separated results and compact tables of descriptive statistics (to APA-style reports).

Takes a distance matrix and plots it as an interactive graph. One point is focused at the center of the graph, around which all other points are plotted in their exact distances as given in the distance matrix. All other non-focus points are plotted as best as possible in relation to one another. Double click on any point to choose a new focus point, and hover over points to see their ID labels. If color label categories are given, hover over colors in the legend to highlight only those points and click on colors to highlight multiple groups. For more information on the rationale and mathematical background, as well as an interactive introduction, see <https://lea-urpa.github.io/focusedMDS.html>.