This package provides methods and functions to implement a Recommendation System based on Collaborative Filtering Methodology. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.

The Certifiably Optimal RulE ListS (Corels) learner by Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision rules with an optimality guarantee, and is made available to R with this package. See the file AUTHORS for a list of copyright holders and contributors.

Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.

Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.

Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>, Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> and Wallot (2018) <doi: 10.1080/00273171.2018.1512846> for further details about the method.

This package provides functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations.

It is designed to streamline the process of calculating complete annual growth rates with user-friendly functions and robust algorithms. It enables researchers and analysts to effortlessly generate precise growth rate estimates for their data. For method details see, Sharma, M.K.(2013) <https://www.indianjournals.com/ijor.aspx?target=ijor:jfl&volume=26&issue=1and2&article=018>. It offers a comprehensive suite of functions and customisable parameters. Equipped to handle varying complexities in data structures. It empowers users to uncover insightful growth dynamics and make informed decisions.

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

Computes the uniform rate of profit, the vector of price of production and the vector of labor values; and also compute measures of deviation between relative prices of production and relative values. <https://scholarworks.umass.edu/econ_workingpaper/347/>. You provide the input-output data and clptheory does the calculations for you.

Generate mean and median weighted or unweighted spatial centers. Functions are analogous to their identically named counterparts within ArcGIS Pro'. Median center methodology based off of Kuhn and Kuenne (1962) <doi:10.1111/j.1467-9787.1962.tb00902.x>.

Comparison of two ROC curves through the methodology proposed by Ana C. Braga.

Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. (2024) <doi:10.48550/arXiv.2403.18602>. Use the main wrapper function `bs()` to build and select a multi-omics network.

This package provides functions and data files to help CE Public-Use Microdata (PUMD) users calculate annual estimated expenditure means, standard errors, and quantiles according to the methods used by the CE with PUMD. For more information on the CE please visit <https://www.bls.gov/cex>. For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For further information about CE PUMD please visit <https://www.bls.gov/cex/pumd.htm>.

This package provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) <doi:10.1007/s00477-015-1079-4>) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) <doi:10.1201/9781420010909>), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) <doi:10.1080/03610918.2010.508860>) and the combination of both, that is, the closure principle computational approach test.

Analyzes spatial transcriptomic data using cells-by-genes and cell location matrices to find gene pairs that coordinate their expression between spatially adjacent cells. It enables quantitative analysis and graphical assessment of these cross-expression patterns. See Sarwar et al. (2025) <doi:10.1101/2024.09.17.613579> and <https://github.com/gillislab/CrossExpression/> for more details.

This package contains functions to help in selecting and exploring features ( or variables ) in binary classification problems. Provides functions to compute and display information value and weight of evidence (WoE) of the variables , and to convert numeric variables to categorical variables by binning. Functions are also provided to determine which levels ( or categories ) of a categorical variable can be collapsed (or combined ) based on their response rates. The functions provided only work for binary classification problems.

Tool for performing computational testing for conditional independence between variables in a dataset. CCI implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) <doi:10.3390/a17080323>.

Responsive and modern HTML card essentials for shiny applications and dashboards. This novel card component in Bootstrap provides a flexible and extensible content container with multiple variants and options for building robust R based apps e.g for graph build or machine learning projects. The features rely on a combination of JQuery <https://jquery.com> and CSS styles to improve the card functionality.

This package provides a lightweight data validation and testing toolkit for R. Its guiding philosophy is that adding code-based data checks to users existing workflow should be both quick and intuitive. The suite of functions included therefore mirror the common data checks many users already perform by hand or by eye. Additionally, the checkthat package is optimized to work within tidyverse data manipulation pipelines.

This package provides methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

This package provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>).

Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes.