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.

This package provides functions and example datasets for Fechnerian scaling of discrete object sets. User can compute Fechnerian distances among objects representing subjective dissimilarities, and other related information. See package?fechner for an overview.

This package provides a collection of utility functions for working with Year Month Day objects. Includes functions for fast parsing of numeric and character input based on algorithms described in Hinnant, H. (2021) <https://howardhinnant.github.io/date_algorithms.html> as well as a branchless calculation of leap years by Jerichaux (2025) <https://stackoverflow.com/a/79564914>.

This package provides color palettes designed to be reminiscent of text on paper. The color schemes were taken from <https://stephango.com/flexoki>. Includes discrete, continuous, and binned scales that are not necessarily color-blind friendly. Simple scale and theme functions are available for use with ggplot2'.

R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.

This package provides a guarded resampling workflow for training and evaluating machineâ learning models. When the guarded resampling path is used, preprocessing and model fitting are reâ estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the tidymodels ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoMLâ style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and userâ controlled.

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.

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

This package implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

This package provides a collection of functions for testing various aspects of univariate time series including independence and neglected nonlinearities. Further provides functions to investigate the chaotic behavior of time series processes and to simulate different types of chaotic time series maps.

This package provides tools to support systematic and reproducible workflows for both stationary and nonstationary flood frequency analysis, with applications extending to other hydroclimate extremes, such as precipitation frequency analysis. This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) <doi:10.1016/j.envsoft.2024.105940>, originally developed in MATLAB', now adapted for the R environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro.

The Fill-Mask Association Test ('FMAT') <doi:10.1037/pspa0000396> is an integrative, probability-based social computing method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositional semantic representations in natural language. Supported language models include BERT <doi:10.48550/arXiv.1810.04805> and its variants available at Hugging Face <https://huggingface.co/models?pipeline_tag=fill-mask>. Methodological references and installation guidance are provided at <https://psychbruce.github.io/FMAT/>.

This package implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records <doi:10.1017/S0003055418000783> and is available at <https://imai.fas.harvard.edu/research/linkage.html>.

This package provides functions for finding smooth interpolating curves connecting a series of points in the plane. Curves may be open or closed, that is, with the first and last point of the curve at the initial point.

Streamlines Quarto workflows by providing tools for consistent project setup and documentation. Enables portability through reusable metadata, automated project structure creation, and standardized templates. Features include enhanced project initialization, pre-formatted Quarto documents, inclusion of Quarto brand functionality, comprehensive data protection settings, custom styling, and structured documentation generation. Designed to improve efficiency and collaboration in R data science projects by reducing repetitive setup tasks while maintaining consistent formatting across multiple documents.

Robust estimation methods for the mean vector, scatter matrix, and covariance matrix (if it exists) from data (possibly containing NAs) under multivariate heavy-tailed distributions such as angular Gaussian (via Tyler's method), Cauchy, and Student's t distributions. Additionally, a factor model structure can be specified for the covariance matrix. The latest revision also includes the multivariate skewed t distribution. The package is based on the papers: Sun, Babu, and Palomar (2014); Sun, Babu, and Palomar (2015); Liu and Rubin (1995); Zhou, Liu, Kumar, and Palomar (2019); Pascal, Ollila, and Palomar (2021).

Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes. Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models. The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).

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>.

Some functions of ade4 and stats are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. In order to permit different weights of the elements to be clustered, the function kmeansW', programmed in C++, is included. It is a modification of kmeans'. Some graphical functions include the option: gg=FALSE'. When gg=TRUE', they use the ggplot2 and ggrepel packages to avoid the super-position of the labels.

This package provides a tool to use a principal component analysis on radially averaged two dimensional Fourier spectra to characterize image texture. The method within the context of ecology was first described by Couteron et al. (2005) <doi:10.1111/j.1365-2664.2005.01097.x> and expanded upon by Solorzano et al. (2018) <doi:10.1117/1.JRS.12.036006> using a moving window approach.

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>.

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.

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core fastverse consists of 4 packages: data.table', collapse', kit and magrittr', that jointly only depend on Rcpp'. The fastverse can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.