The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), <doi:10.1093/bioinformatics/bti817>.

This package contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.

CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the errors', units and/or quantities packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) <https://physics.nist.gov/cuu/Constants/>.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by Huang et al. (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

The concaveman function ports the concaveman (<https://github.com/mapbox/concaveman>) library from mapbox'. It computes the concave polygon(s) for one or several set of points.

This package provides function to create, read, write, and work with iCalendar files (which typically have .ics or .ical extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. iCalendar is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.

Simulate species occurrence and abundances (counts) along gradients.

This package implements methods for querying data from CalPASS using its API. CalPASS Plus. MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.

This package provides methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.

Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.

The vctrs package provides a concept of vector prototype that can be especially useful when deploying models and code. Serialize these object prototypes to JSON so they can be used to check and coerce data in production systems, and deserialize JSON back to the correct object prototypes.

This package provides tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection.

Find the location of the code for an R package based on the package's name or string representation. Checks on CRAN based on information in the URL field or BioConductor and GitHub based on constructing a URL, and verifies all paths via testing for a successful response. This can be useful when automating static code analysis based on a list of package names, and similar tasks.

Calculates the co-ranking matrix to assess the quality of a dimensionality reduction.

It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Client for CKAN API (<https://ckan.org/>). Includes interface to CKAN APIs for search, list, show for packages, organizations, and resources. In addition, provides an interface to the datastore API.

This package provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>. The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities. These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares. CoDaImpact is designed to be used with the compositions package and its ecosystem.

Estimate and return the needed parameters for visualisations designed for OpenBudgets <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the OpenBudgets data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to JSON format and use them as input in a different graphical interface.

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>.

Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. (2002) <doi:10.1016/S0167-9473(02)00058-0>.

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

Effortless multicollinearity management in data frames with both numeric and categorical variables for statistical and machine learning applications. The package simplifies multicollinearity analysis by combining four robust methods: 1) target encoding for categorical variables (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); 2) automated feature prioritization to prevent key variable loss during filtering; 3) pairwise correlation for all variable combinations (numeric-numeric, numeric-categorical, categorical-categorical); and 4) fast computation of variance inflation factors.

Render tables in text format in the terminal using ANSI strings thanks to the cli and crayon packages.