This package provides a joint mixture model has been developed by Majumdar et al. (2025) <doi:10.48550/arXiv.2412.17511> that integrates information from gene expression data and methylation data at the modelling stage to capture their inherent dependency structure, enabling simultaneous identification of differentially methylated cytosine-guanine dinucleotide (CpG) sites and differentially expressed genes. The model leverages a joint likelihood function that accounts for the nested structure in the data, with parameter estimation performed using an expectation-maximisation algorithm.

Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations.

This package provides a straightforward interface for accessing the IMF (International Monetary Fund) data JSON API, available at <https://data.imf.org/>. This package offers direct access to the primary API endpoints: Dataflow, DataStructure, and CompactData. And, it provides an intuitive interface for exploring available dimensions and attributes, as well as querying individual time-series datasets. Additionally, the package implements a rate limit on API calls to reduce the chances of exceeding service limits (limited to 10 calls every 5 seconds) and encountering response errors.

An implementation of the International Association for the Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use and on the releases for viscosity, conductivity, surface tension and melting pressure.

Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the iucnrdata package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').

This package provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.

This package provides functions to Interact with the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk/web/index.aspx>.

Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the icd package for finding comorbidities.

This package provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).

Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi10.1002/sim.7350 and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi10.1093/biostatistics/kxaa052. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.

Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) <doi:10.1051/cagri/2019004> ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials.

Addresses the log of zero by developing a new family of estimators called iterated Ordinary Least Squares. This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular log(Y + 1) transformation. For more details about how to use it, see the notebook at: <https://www.davidbenatia.com/>.

Estimate confidence intervals for mean, proportion, mean difference for unpaired and paired samples and proportion difference. Plot the confidence intervals. Generate documents explaining the statistical result step by step.

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

Interesting igraph datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.

Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. icdpicr contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. ICDPIC was originally developed in Stata', and icdpicr is an open-access update that accepts both ICD-9 and ICD-10 codes.

Four datasets are provided here from the Intendo game Super Jetroid'. It is data from the 2015 year of operation and it comprises a revenue table ('all_revenue'), a daily users table ('users_daily'), a user summary table ('user_summary'), and a table with data on all user sessions ('all_sessions'). These core datasets come in different sizes, and, each of them has a variant that was intentionally made faulty (totally riddled with errors and inconsistencies). This suite of tables is useful for testing with packages that focus on data validation and data documentation.

This package provides ability to create color palettes from image files. It offers control over the type of color palette to derive from an image (qualitative, sequential or divergent) and other palette properties. Quantiles of an image color distribution can be trimmed. Near-black or near-white colors can be trimmed in RGB color space independent of trimming brightness or saturation distributions in HSV color space. Creating sequential palettes also offers control over the order of HSV color dimensions to sort by. This package differs from other related packages like RImagePalette in approaches to quantizing and extracting colors in images to assemble color palettes and the level of user control over palettes construction.

This package provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". The datasets can be used to present various methods of data analysis and statistical modeling. Functions for data visualization are also implemented.

This package implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalized, and standard IB clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2024) <doi:10.48550/arXiv.2407.03389>. The standard IB method originates from Tishby et al. (2000) <doi:10.48550/arXiv.physics/0004057>, the agglomerative variant from Slonim and Tishby (1999) <https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>, and the generalized IB from Strouse and Schwab (2017) <doi:10.1162/NECO_a_00961>.

An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). The algorithm is explained at <doi:10.5201/ipol.2012.gjmr-lsd>.

This package provides a pipeline to annotate chromatography peaks from the IDSL.IPA workflow <doi:10.1021/acs.jproteome.2c00120> with molecular formulas of a prioritized chemical space using an isotopic profile matching approach. The IDSL.UFA workflow only requires mass spectrometry level 1 (MS1) data for formula annotation. The IDSL.UFA methods was described in <doi:10.1021/acs.analchem.2c00563> .

Uses data and researcher's beliefs on measurement error and instrumental variable (IV) endogeneity to generate the space of consistent beliefs across measurement error, instrument endogeneity, and instrumental relevance for IV regressions. Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.

Compute onestep and multistep time series forecasts for machine learning models.