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>.

Set chunk hooks for R Markdown documents <https://rmarkdown.rstudio.com/>, and improve user experience. For example, change units of figure sizes, benchmark chunks, and number lines on code blocks.

An R interface to Cheetah Grid', a high-performance JavaScript table widget. cheetahR allows users to render millions of rows in just a few milliseconds, making it an excellent alternative to other R table widgets. The package wraps the Cheetah Grid JavaScript functions and makes them readily available for R users. The underlying grid implementation is based on Cheetah Grid <https://github.com/future-architect/cheetah-grid>.

This package provides functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).

Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.

This package provides a collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, Vidoli and Fusco and Mazziotta <doi:10.1007/s11205-014-0710-y>, Mazziotta and Pareto (2016) <doi:10.1007/s11205-015-0998-2>, Van Puyenbroeck and Rogge <doi:10.1016/j.ejor.2016.07.038> and other authors.

CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in performing metabolite annotation. cmmr (CEU Mass Mediator RESTful API) allows for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc. For more information about the API Endpoint please go to <https://github.com/YaoxiangLi/cmmr>.

An implementation of Fan plots for cytometry data in ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) <doi:10.1007/s11222-018-9820-8>.

In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from AllMetrics package. â input_dfâ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular modelâ s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as â MINâ and other values are denoted as â NAâ . Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as â MAXâ and other values are denoted as â NAâ . â output_dfâ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of â input_dfâ . Values in â output_dfâ are corresponding â NAâ , â MINâ or â MAXâ . Finally, the column containing minimum number of â NAâ values is denoted as the best column. â min_NA_colâ gives the name of the best column (model). â min_NA_valuesâ are the corresponding metrics values. âBestColumn_metricsâ is the data frame (dimension: 1*8) containing different metrics of the best column (model). â best_column_resultsâ is the final result (a list) containing all of these output elements. In special case, if two columns having equal NA', it will be checked among these two column which one is having least NA in first five rows and will be inferred as the best. More details about AllMetrics can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.

Nonparametric two-sample procedure for comparing survival quantiles.

After using this, a publication-ready correlation table with p-values indicated will be created. The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the Hmisc framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a .csv2 with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult <https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/>.

This package provides functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) <doi:10.1016/j.wace.2018.01.002>.

Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website <https://www.longpaddock.qld.gov.au/>. This data is freely available under the Creative Commons 4.0 licence.

This package provides tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references: Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007)<doi:10.1214/009053606000001424> Zhang, L. X. & Hu, F. (2009) <doi:10.1007/s11766-009-0001-6> Hu, J., Zhu, H., & Hu, F. (2015) <doi:10.1080/01621459.2014.903846> Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) <doi:10.1002/pst.2160> Mukherjee, A., Jana, S., & Coad, S. (2024) <doi:10.1177/09622802241287704>.

P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) <doi:10.1007/s00477-015-1079-4>) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) <doi:10.1201/9781420010909>), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) <doi:10.1080/03610918.2010.508860>) and the combination of both, that is, the closure principle computational approach test.

Simulate one or many frequentist confidence clinical trials based on a specified set of parameters. From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characterisitics over thousands of instances.

This package provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, ComBatFamQC enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) <doi:10.1093/biostatistics/kxj037>, Beer et al., (2020) <doi:10.1016/j.neuroimage.2020.117129>, Pomponio et al., (2020) <doi:10.1016/j.neuroimage.2019.116450>, and Chen et al., (2021) <doi:10.1002/hbm.25688>.

This is an add-on to the cna package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) <doi:10.1177/0049124121995554>.

Procedures include Phillips (1995) FMVAR <doi:10.2307/2171721>, Kitamura and Phillips (1997) FMGMM <doi:10.1016/S0304-4076(97)00004-3>, Park (1992) CCR <doi:10.2307/2951679>, and so on. Tests with 1 or 2 structural breaks include Gregory and Hansen (1996) <doi:10.1016/0304-4076(69)41685-7>, Zivot and Andrews (1992) <doi:10.2307/1391541>, and Kurozumi (2002) <doi:10.1016/S0304-4076(01)00106-3>.

This package provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>. Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.

Returns an edit-distance based clusterization of an input vector of strings. Each cluster will contain a set of strings w/ small mutual edit-distance (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by stringdist::stringdist(). The set of all mutual edit-distances is then used by graph algorithms (from package igraph') to single out subsets of high connectivity.