This package contains greedy algorithms for coarse approximation linear functions.

Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece.

This package provides a framework for specifying and running flexible linear-time reachability-based algorithms for graphical causal inference. Rule tables are used to encode and customize the reachability algorithm to typical causal and probabilistic reasoning tasks such as finding d-connected nodes or more advanced applications. For more information, see Wienöbst, Weichwald and Henckel (2025) <doi:10.48550/arXiv.2506.15758>.

Common API for filtering data stored in different data models. Provides multiple filter types and reproducible R code. Works standalone or with shinyCohortBuilder as the GUI for interactive Shiny apps.

Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. <doi:10.1007/s11135-025-02351-z>.

Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2024.

This package contains an administrative-level-1 map of the world. Administrative-level-1 is the generic term for the largest sub-national subdivision of a country. This package was created for use with the choroplethr package.

This package provides a fast way to loop a character vector or file names as a menu in the console for the user to choose an option.

This package provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using CES datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from multiple sources including the Borealis Data repository <https://borealisdata.ca/> and the official Canadian Election Study website <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated with the Canadian Election Study, Borealis Data, or the University of British Columbia, and users should cite the original data sources in their work.

Integrates two numerical omics data sets from the same samples using partial correlations. The output can be represented as a network, bipartite graph or a hypergraph structure. The method used in the package refers to Klaus et al (2021) <doi:10.1016/j.molmet.2021.101295>.

Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) <doi:10/cr3qwn>; or Hargreaves and Samani (1985) <doi:10.13031/2013.26773>. Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well.

This package provides a big data version for fitting cumulative probability models using the orm() function from the rms package. See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.

This package implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

This package implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.

Providing a set of functions to easily generate and iterate complex networks. The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.

This package provides functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method. Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, <doi:10.18637/jss.v094.i07>).

This package provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).

Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review.

Provide the safe color set for color blindness, the simulator of protanopia, deuteranopia. The color sets are collected from: Wong, B. (2011) <doi:10.1038/nmeth.1618>, and <http://mkweb.bcgsc.ca/biovis2012/>. The simulations of the appearance of the colors to color-deficient viewers were based on algorithms in Vienot, F., Brettel, H. and Mollon, J.D. (1999) <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>. The cvdPlot() function to generate ggplot grobs of simulations were modified from <https://github.com/clauswilke/colorblindr>.

Encode and decode c-squares, from and to simple feature (sf) or spatiotemporal arrays (stars) objects. Use c-squares codes to quickly join or query spatial data.

Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator, probability density function, or data. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.

Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default. The plot features rely on the heatmap.n2() function in the heatmapFlex package.

Bayesian fit of a Dirichlet Process Mixture with hierarchical multivariate skew normal kernels and coarsened posteriors. For more information, see Gorsky, Chan and Ma (2024) <doi:10.1214/22-BA1356>.