R's implementation of the JavaScript library path-to-regexp', it aims to provide R web frameworks features such as parameter handling among other URL path utilities.

An add-on to the party package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the party package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the party implementation. The package is compatible with random forests fit using the party and the randomForest package. The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" <doi:10.48550/arXiv.1506.01286> by TD Hocking and G Bourque.

Loads and processes huge text corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.

This package provides a user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.

This package implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.

This package provides a broad-view perspective on data via linear mapping of data onto a radial coordinate system. The package contains functions to visualize the residual values of linear regression and Cartesian data in the defined radial scheme. See the pacviz documentation page for more information: <https://pacviz.sriley.dev/>.

This is a data only package, that provides distances from a paper plane experiment.

Defines aesthetically pleasing colour palettes.

Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting.

Crop production systems are increasingly challenged by climate variability, resource limitations, and bioticâ abiotic stresses. In this context, stress tolerance indices and physiological trait estimators are essential tools to identify stable and superior genotypes, quantify yield stability under stress versus non-stress conditions, and understand plant adaptive responses. The PhysioIndexR package provides a unified framework to compute commonly used stress indices, physiological traits, and derived metrics that are critical in crop improvement, crop physiology, and other agricultural sciences. The package includes functions to calculate classical stress tolerance indices (See Lamba et al., 2023; <doi:10.1038/s41598-023-37634-8>) such as Tolerance (TOL), Stress Tolerance Index (STI), Stress Susceptibility Percentage Index (SSPI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Mean Relative Performance (MRP), and Percent Yield Reduction (PYR), along with a convenience wrapper all_indices() that returns all indices simultaneously. The function mfvst_from_indices() integrates these indices into a composite stress score using direction-aware membership values (0â 1 scaling) and also averaging, facilitating genotype ranking and selection (See Vinu et al., 2025; <doi:10.1007/s12355-025-01595-1>). The package also implements two novel composite functions: WMFVST(), which computes the Weighted Mean Membership Function Value for Stress Tolerance, and WASI(), which computes the Weighted Average Stress Index, both derived from membership function values (MFV) and raw stress index values, respectively. Beyond stress indices, the package provides functions for key physiological traits relevant to sugarcane and other crops: bmap() computes biomass accumulation and partitioning between leaf, cane/shoot, and root fractions. chl() estimates total chlorophyll content from Soil-Plant Analysis Development (SPAD) and Chlorophyll Content Index (CCI) values using validated quadratic models particularly for sugarcane (See Krishnapriya et al., 2020; <doi:10.37580/JSR.2019.2.9.150-163>). ctd() calculates canopy temperature depression (CTD) from ambient and canopy temperatures, an important indicator of transpiration efficiency. growth() computes key growth analysis parameters, including Leaf Area Index (LAI), Net Assimilation Rate (NAR), and Crop Growth Rate (CGR) across crop growth stages (See Watson, 1958; <doi:10.1093/oxfordjournals.aob.a083596>). ranking() provides flexible ranking utilities for genotype performance with multiple tie-handling and NA-placement options. Through these tools, the package enables researchers to: (i) quantify crop responses to stress environments, (ii) partition physiological components of yield, (iii) integrate multiple indices into composite metrics for genotype evaluation, and (iv) facilitate informed decision making in breeding pipelines, and plant physiology experiments. By combining physiology-based traits with quantitative stress indices, PhysioIndexR supports comprehensive crop evaluation and helps researchers identify multi-stress-resilient superior genotypes, thereby contributing to genetic improvement and ensuring sustainable production of food, fuel, and fibre in the era of limited resources and climate change.

This package provides a collection of functions and data sets that support teaching a quantitative finance MS level course on Portfolio Construction and Risk Analysis, and the writing of a textbook for such a course. The package is unique in providing several real-world data sets that may be used for problem assignments and student projects. The data sets include cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures data from S&P Global, and several SP500 data sets.

This package provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.

This package provides a set of palettes imported from Gimp distributed under GPL3 (<https://www.gimp.org/about/COPYING>), and Inkscape distributed under GPL2 (<https://inkscape.org/about/license/>).

This package provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of palaeoverse is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

This package provides a set of Analysis Data Model (ADaM) datasets constructed by modifying the ADaM datasets in the pharmaverseadam package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming.

Efficient implementations of multiple exact and approximate methods as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao & Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalized Poisson binomial distribution.

Implementation of the Partitioned Local Depth (PaLD) approach which provides a measure of local depth and the cohesion of a point to another which (together with a universal threshold for distinguishing strong and weak ties) may be used to reveal local and global structure in data, based on methods described in Berenhaut, Moore, and Melvin (2022) <doi:10.1073/pnas.2003634119>. No extraneous inputs, distributional assumptions, iterative procedures nor optimization criteria are employed. This package includes functions for computing local depths and cohesion as well as flexible functions for plotting community networks and displays of cohesion against distance.

Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.

Periodic B Splines Basis.

Plots matrices of colours as grids of coloured squares - aka heatmaps, guaranteeing legible row and column names, without transformation of values, without re-ordering rows or columns, and without dendrograms.

This package provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.

An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. Lutkepohl, H. (2007, ISBN:3540262393).

This package provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.