In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the CDsampling package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.

Many correlation coefficient related functions are offered, such as correlations, partial correlations and hypothesis testing using asymptotic tests and computer intensive methods (bootstrap and permutation). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press and Owen A. B. (2001). "Empirical likelihood". Chapman and Hall/CRC Press. ISBN: 9781584880714.

Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.

This package implements a joint cointegration testing approach that combines Engle-Granger, Johansen maximum eigenvalue, Boswijk, and Banerjee tests into a unified test-statistic for the null of non-cointegration. Also see Bayer and Hanck (2013) <doi:10.1111/j.1467-9892.2012.00814.x>.

This package provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>. The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities. These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares. CoDaImpact is designed to be used with the compositions package and its ecosystem.

It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. It also calculates the expected effect and the probability of observing the composite endpoint, among others. The methodology can be found in Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.

This package contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. CASMI is a fundamental concept of a series of methods. For more information about CASMI and CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.

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>.

This package provides a programmatic interface to the Chromosome Counts Database (<https://ccdb.tau.ac.il/>), Rice et al. (2014) <doi:10.1111/nph.13191>. This package is part of the ROpenSci suite (<https://ropensci.org>).

Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package mlt', technical details are given in Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. The package also contains an experimental implementation of multivariate count transformation models with an application to multi-species distribution models <DOI:10.48550/arXiv.2201.13095>.

Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at <https://covid.saude.gov.br/>, is available at the country, region, state, and city levels. The package also downloads world-level COVID-19 data from Johns Hopkins University's repository. COVID-19 data is available from the start of follow-up until to May 5, 2023, when the World Health Organization (WHO) declared an end to the Public Health Emergency of International Concern (PHEIC) for COVID-19.

Quickly estimate the net growth rate of a population or clone whose growth can be approximated by a birth-death branching process. Input should be phylogenetic tree(s) of clone(s) with edge lengths corresponding to either time or mutations. Based on coalescent results in Johnson et al. (2023) <doi:10.1093/bioinformatics/btad561>. Simulation techniques as well as growth rate methods build on prior work from Lambert A. (2018) <doi:10.1016/j.tpb.2018.04.005> and Stadler T. (2009) <doi:10.1016/j.jtbi.2009.07.018>.

Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').

The Certifiably Optimal RulE ListS (Corels) learner by Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision rules with an optimality guarantee, and is made available to R with this package. See the file AUTHORS for a list of copyright holders and contributors.

The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research.

Estimate and return the needed parameters for visualisations designed for OpenBudgets <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the OpenBudgets data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to JSON format and use them as input in a different graphical interface.

Create rich command line applications, with colors, headings, lists, alerts, progress bars, etc. It uses CSS for custom themes. This package is now superseded by the cli package. Please use cli instead in new projects.

This package implements cointegration/co-trending rank selection algorithm in Guo and Shintani (2013) "Consistent co-trending rank selection when both stochastic and nonlinear deterministic trends are present". The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.

This package provides functions and data files to help CE Public-Use Microdata (PUMD) users calculate annual estimated expenditure means, standard errors, and quantiles according to the methods used by the CE with PUMD. For more information on the CE please visit <https://www.bls.gov/cex>. For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For further information about CE PUMD please visit <https://www.bls.gov/cex/pumd.htm>.

Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC.

Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. (1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm from the MASS package).