Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.

This package contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.

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 a significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.

This package provides functions for classical test theory analysis, following methods presented by Wu et al. (2006) <doi:10.1007/978-981-10-3302-5>.

With this package you can run ConMET locally in R. ConMET is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. ConMET automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the lavaan package (Rosseel, 2012) to run CFAs.

This package provides tools for connecting to CHILDES', an open repository for transcripts of parent-child interaction. For more information on the underlying data, see <https://langcog.github.io/childes-db-website/>.

Read tables chunk by chunk using a C++ backend and a simple R interface.

This package provides a system for creating R Markdown reports with a sequential syntax.

One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.

Includes climate data from Japan Meteorological Agency ('JMA') <https://www.jma.go.jp/jma/indexe.html>. Can download climate data from JMA'.

Use the US Census API to collect summary data tables for SF1 and ACS datasets at arbitrary geographies.

This package provides a Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses.

Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.

Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.

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.

This package provides data science tools for conservation science, including methods for environmental data analysis, humidity calculations, sustainability metrics, engineering calculations, and data visualisation. Supports conservators, scientists, and engineers working with cultural heritage preventive conservation data. The package is motivated by the framework outlined in Cosaert and Beltran et al. (2022) "Tools for the Analysis of Collection Environments" <https://www.getty.edu/conservation/publications_resources/pdf_publications/tools_for_the_analysis_of_collection_environments.html>.

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Enables user interactivity with large-language models ('LLM') inside the RStudio integrated development environment (IDE). The user can interact with the model using the shiny app included in this package, or directly in the R console. It comes with back-ends for OpenAI', GitHub Copilot', and LlamaGPT'.

This package provides a set of functions to manage CRAN'-like repositories efficiently.

Convert BCD (raw bytes) to decimal numbers and vice versa. BCD format is used to preserve decimals exactly, as opposed to the binary rounding errors inherent in "numeric" or "floating-point" formats.

Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approachesâ Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glazâ along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.

Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.

This package provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). CREAM uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. CREAM considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.