This package provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.

Sample size calculations for practical equivalence trial design with a time to event endpoint.

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.

Helpers for preparing ecommerce product photo planning tables, prompt sheets, and public ProductShot AI workflow URLs. The package works offline and focuses on data-frame preparation for product image batches.

Allow to run pylint on Python files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file.

This package provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields.

An implementation of the "Design Analysis" proposed by Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines the evaluation of Power-Analysis with other inferential-risks as Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.

An application to calculate a patient's pretest probability (PTP) for obstructive Coronary Artery Disease (CAD) from a collection of guidelines or studies. Guidelines usually comes from the American Heart Association (AHA), American College of Cardiology (ACC) or European Society of Cardiology (ESC). Examples of PTP scores that comes from studies are the 2020 Winther et al. basic, Risk Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery Calcium Score-weighted Clinical Likelihood (CACS-CL) models <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>. As diagnosis of CAD involves a costly and invasive coronary angiography procedure for patients, having a reliable PTP for CAD helps doctors to make better decisions during patient management. This ensures high risk patients can be diagnosed and treated early for CAD while avoiding unnecessary testing for low risk patients.

Perform a supervised data analysis on a database through a shiny graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.

Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency.

Extends ggplot2 to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.

Spatial estimation of a prevalence surface or a relative risks surface, using data from a Demographic and Health Survey (DHS) or an analog survey, see Larmarange et al. (2011) <doi:10.4000/cybergeo.24606>.

Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

Estimate sample size based on precision rather than power. precisely is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. precisely is based on the work of Rothman and Greenland (2018).

Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.

Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>. PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) <doi:10.48550/arXiv.2503.23165>.

This package provides tools for constructing detailed synthetic human populations from frequency tables. Add ages based on age groups and sex, create households, add students to education facilities, create employers, add employers to employees, and create interpersonal networks.

Make statistical inference on the probability of being in response, the duration of response, and the cumulative response rate up to a given time point. The method can be applied to analyze phase II randomized clinical trials with the endpoints being time to treatment response and time to progression or death.

This package provides tools for anonymizing sensitive patient and research data. Helps protect privacy while keeping data useful for analysis. Anonymizes IDs, names, dates, locations, and ages while maintaining referential integrity. Methods based on: Sweeney (2002) <doi:10.1142/S0218488502001648>, Dwork et al. (2006) <doi:10.1007/11681878_14>, El Emam et al. (2011) <doi:10.1371/journal.pone.0028071>, Fung et al. (2010) <doi:10.1145/1749603.1749605>.

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. <doi:10.1214/14-AOS1255>. Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. <doi:10.1080/10618600.2021.1938585>. Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. <ISBN:9781482242744>. Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. <doi:10.48550/arXiv.2506.15659>.

Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous United States for user-defined time ranges. Provides functions to retrieve pollutant data from the U.S. Environmental Protection Agencyâ s Air Quality System (AQS) API service <https://aqs.epa.gov/aqsweb/documents/data_api.html> for interactive visualization through a shiny application, allowing users to explore pollutant levels for a given location over time relative to the National Ambient Air Quality Standards (NAAQS).

Create random passwords of letters, numbers and punctuation.