Constrained ordinary least squares is performed. One constraint is that all beta coefficients (including the constant) cannot be negative. They can be either 0 or strictly positive. Another constraint is that the sum of the beta coefficients equals a constant. References: Hansen, B. E. (2022). Econometrics, Princeton University Press. <ISBN:9780691235899>.

Create rich command line applications, with colors, headings, lists, alerts, progress bars, etc. It uses CSS for custom themes. This package is now superseded by the cli package. Please use cli instead in new projects.

Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.

This package implements correlation structures for symmetric relational data (e.g. pairwise distances and dissimilarities) that interface with models using nlme'-style correlation structures. The maximum likelihood population effects method is described by Clarke et al. (2002) <doi:10.1198/108571102320>.

Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. The methods for phylogenetic and functional nestedness is described in Melo, Cianciaruso and Almeida-Neto (2014) <doi:10.1111/2041-210X.12185>. This should be a complement to available packages, particularly vegan'.

Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench <doi:10.1002/psp4.70067>. Execution is supported both locally or on remote machines.

This package provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.

Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and mirai (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, terra (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and sf (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

Read Condensed Cornell Ecology Program ('CEP') and legacy CANOCO files into R data frames.

This package implements a changepoint-aware ensemble forecasting algorithm that combines Theta, TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend, Seasonal components), and ARFIMA (AutoRegressive, Fractionally Integrated, Moving Average) using a product-of-experts approach for robust probabilistic prediction.

This package provides tools for working with the International Classification of Diseases ('ICD-10 Chile official MINSAL'/'DEIS v2018). Includes optimized SQL search with SQLite', fuzzy matching of medical terms ('Jaro-Winkler'), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS <https://deis.minsal.cl/centrofic/>.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Chemical analysis of proteins based on their amino acid compositions. Amino acid compositions can be read from FASTA files and used to calculate chemical metrics including carbon oxidation state and stoichiometric hydration state, as described in Dick et al. (2020) <doi:10.5194/bg-17-6145-2020>. Other properties that can be calculated include protein length, grand average of hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002 <doi:10.1073/pnas.062526999>; Wagner, 2005 <doi:10.1093/molbev/msi126>; Zhang et al., 2018 <doi:10.1038/s41467-018-06461-1>). A database of amino acid compositions of human proteins derived from UniProt is provided.

This package provides a tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.

General functions for convolutions of data. Moving average, running median, and other filters are available. Bibliography regarding the functions can be found in the following text. Richard G. Brereton (2003) <ISBN:9780471489771>.

The COVID Symptom Study is a non-commercial project that uses a free mobile app to facilitate real-time data collection of symptoms, exposures, and risk factors related to COVID19. The package allows easy access to summary statistics data from COVID Symptom Study Sweden.

This package provides a Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see Manipur et al (2024, Nature Communications) <doi:10.1038/s41467-024-49990-8>.

This package provides functions to perform missing value imputation with compositional data using the Jensen-Shannon divergence based k--NN and a--k--NN algorithms. The functions are based on the following paper: Tsagris M., Alenazi A. and Stewart C. (2026). "A Jensen--Shannon divergence based k--NN algorithm for missing value imputation in compositional data", <doi:10.1080/02664763.2026.2677908>.

This package provides methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.

This package implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.

This package implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2025) <doi:10.1093/biomet/asaf024> for description of the NP-MOJO methodology.