Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <doi:10.1016/j.softx.2024.101921>.

This package provides a flexible interface for interacting with Large Language Model ('LLM') providers including OpenAI', Azure OpenAI', Azure AI Foundry', Groq', Anthropic', DeepSeek', DashScope', Gemini', Grok', GitHub Models', and AWS Bedrock. Supports both synchronous and asynchronous chat-completion APIs, with features such as retry logic, dynamic model selection, customizable parameters, and multi-message conversation handling. Designed to streamline integration with state-of-the-art LLM services across multiple platforms.

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>.

Uses non-linear regression to statistically compare two circadian rhythms. Groups are only compared if both are rhythmic (amplitude is non-zero). Performs analyses regarding mesor, phase, and amplitude, reporting on estimates and statistical differences, for each, between groups. Details can be found in Parsons et al (2020) <doi:10.1093/bioinformatics/btz730>.

Maximum likelihood estimation in respondent driven samples.

This package provides tools for producing data-driven curriculum vitae documents from structured data stored in an Excel workbook. The core workflow reads CV content from a workbook, converts it into Typst layout blocks, and renders a polished PDF via the Quarto publishing system. Includes functions for reading and cleaning CV data, building Typst section headings and entries, rendering CV sections from data frames, and scaffolding new CV projects with a standard folder structure and template workbook. Designed to separate content from layout: CV data lives in the spreadsheet, rendering configuration lives in Quarto', and transformation logic lives in small, reusable R functions. See the Typst typesetting system at <https://typst.app> and the Quarto publishing system at <https://quarto.org>. Inspired by the vitae package <https://CRAN.R-project.org/package=vitae> and the Awesome CV LaTeX template <https://github.com/posquit0/Awesome-CV>.

Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via <doi:10.48550/arXiv.2105.00966>.

This package contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve and the optimal cutoff point for the right and interval censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene and El Ghouch (2022) <doi:10.1002/bimj.202000382>.

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

Evaluates the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF), random numbers and maximum likelihood estimates (MLEs) of well-known complementary binomial-G, complementary negative binomial-G and complementary geometric-G families of distributions taking baseline models such as exponential, extended exponential, Weibull, extended Weibull, Fisk, Lomax, Burr-XII and Burr-X. The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. <doi:10.1186/s40488-016-0052-1>.

This package provides tools for fitting the copCAR (Hughes, 2015) <DOI:10.1080/10618600.2014.948178> regression model for discrete areal data. Three types of estimation are supported (continuous extension, composite marginal likelihood, and distributional transform), for three types of outcomes (Bernoulli, negative binomial, and Poisson).

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>).

CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.

This package provides functions and Data to support Context Driven Exploratory Projection Pursuit.

Engines for survival models from the parsnip package. These include parametric models (e.g., Jackson (2016) <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g., Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).

Harmonizes variables from the Canadian Health Measures Survey (CHMS) across cycles 1-6 (2007-2019), producing consistent, analysis-ready variables for use with CHMS data. Recoding is data-driven through metadata tables and applied with recodeflow::rec_with_table() from the recodeflow package. The recoding approach builds on sjmisc::rec() from the sjmisc package (Ludecke 2018) <doi:10.21105/joss.00754>.

Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.

Estimation of quantile regression models for survival data.

This package implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (Journal of the American Statistical Association 2025, <doi:10.1080/01621459.2024.2422124>).

The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.

This package provides a collection of clinical trial example datasets from multiple sources including the CDISC Pilot 01 study (CDISC <https://www.cdisc.org/>). All datasets are provided in Parquet format for efficient storage and can be accessed using the connector package. Designed for training, testing, prototyping, and demonstrating clinical data analysis workflows.

Collect marketing data from Campaign Manager using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides an interface to the ClinicalOmicsDB API, allowing for easy data downloading and importing. ClinicalOmicsDB is a database of clinical and omics data from cancer patients. The database is accessible at <http://trials.linkedomics.org>.