This package implements a new method ClussCluster descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, ClussCluster incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.

Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier.

Allows users to identify similar cases for qualitative case studies using statistical matching methods.

Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models.

Wrangle country data more effectively and quickly. This package contains functions to easily identify and convert country names, download country information, merge country data from different sources, and make quick world maps.

Mines contiguous sequential patterns in text.

Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on rmarkdown partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins.

This package provides functions to test and compare causal models using Confirmatory Path Analysis.

The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models.

Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.

Randomization-Based Inference for customized experiments. Computes Fisher-Exact P-Values alongside null randomization distributions. Retrieves counternull sets and generates counternull distributions. Computes Fisher Intervals and Fisher-Adjusted P-Values. Package includes visualization of randomization distributions and Fisher Intervals. Users can input custom test statistics and their own methods for randomization. Rosenthal and Rubin (1994) <doi:10.1111/j.1467-9280.1994.tb00281.x>.

Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.

This package contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user.

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

CemCO algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

Fetches the Cornell Lab of Ornithology Open Tree of Life (clootl) tree in a specified taxonomy. Optionally prune it to a given set of study taxa. Provide a recommended citation list for the studies that informed the extracted tree. Tree generated as described in McTavish et al. (2024) <doi:10.1101/2024.05.20.595017>.

Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsaf R-package includes a shiny based interface for an easy application of the cmsafops and cmsafvis packages - the CM SAF R Toolbox. The Toolbox offers an easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

This package provides tools for causal structure learning from observational data, with emphasis on temporally ordered variables. The package implements the Temporal Peterâ Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021; <doi:10.1093/aje/kwab087>), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; <doi:10.48550/arXiv.2502.06232>) and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background knowledge, which can be incorporated into the implemented algorithms from the R packages bnlearn (Scutari, 2010; <doi:10.18637/jss.v035.i03>) and pcalg (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>), as well as the Java library Tetrad (Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>). The package further includes utilities for visualization, comparison, and evaluation of graph structures, facilitating performance evaluation and methodological studies.

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. CimpleG is a method for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. CimpleG is time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type (but users can also select more sites if they so wish). Users can train cell type classifiers ('CimpleG based, and others) and directly apply these in a deconvolution of cell mixes context. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution. For more details see Maié et al. (2023) <doi:10.1186/s13059-023-03000-0>.

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>.

Calculating crude sequence ratio, adjusted sequence ratio and confidence intervals using data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Computation of Multiscale Codependence Analysis and spatial eigenvector maps.