By binding R functions and the Highcharts <http://www.highcharts.com/> charting library, hpackedbubble package provides a simple way to draw split packed bubble charts.

Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.

Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.

Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014). <doi:10.1371/journal.pone.0114089>.

This package implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm, a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD). In addition, this method attaches a confidence interval to assess the uncertainty of its predictions. See Su and Zhu (2018) <arXiv:1802.04876> for details.

This package implements Higher-Order Influence Function (HOIF) estimators of the Average Treatment Effect (ATE), following Robins et al. (2008) <doi:10.1214/193940307000000527>, Liu et al. (2017) <doi:10.48550/arXiv.1705.07577> and Liu and Li (2023) <doi:10.48550/arXiv.2302.08097>. Estimators of any order are supported, with optional covariate basis transformations (B-splines, Fourier) and optional K-fold sample splitting (cross-fitting) for improved finite-sample performance. The core higher-order U-statistics are computed exactly via the ustats package, an R interface to the Python package u-stats'; the underlying algorithm and its computational complexity are analyzed in Chen, Zhang and Liu (2025) <doi:10.48550/arXiv.2508.12627>. A pure R implementation (up to order 6) is also provided as a fallback that does not require Python'.

Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Input multiple versions of a source document, and receive HTML code for a highlighted version of the source document indicating the frequency of occurrence of phrases in the different versions. This method is described in Chapter 3 of Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.

This package provides a data only package containing commercial domestic flights that departed Houston (IAH and HOU) in 2011.

Spatial heterogeneity can be specified in various ways. hspm is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of hspm deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).

This package provides a data set of the Portuguese NHS hospitals.

This package provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.

This package provides a collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.

Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.

Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Create dynamic, data-driven text. Given two values, a list of talking points is generated and can be combined using string interpolation. Based on the glue package.

Miscellaneous convenience functions and wrapper functions to convert frequencies between Hz, semitones, mel and Bark, to create a matrix of dummy columns from a factor, to determine whether x lies in range [a,b], and to add a bracketed line to an existing plot. This package also contains an example data set of a stratified sample of 80 talkers of Dutch.

Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.

The conditional treatment effect for competing risks data in observational studies is estimated. While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) <arXiv:2112.09535>.

Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>, no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) <doi:10.1080/10485250903171621> and tests based on ranks given in Wang and Akritas (2010b) <doi:10.1016/j.jmva.2010.03.012>.

In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.

The hybrid model likelihood as described by Ramos-Goñi et al. (2017) <doi:10.1097/MLR.0000000000000283> is implemented and and embedded in a latent class framework. The package is based on flexmix and among others contains an M-step-driver as described by Leisch (2004) <doi:10.18637/jss.v011.i08>. Users can, for example, estimate latent classes for EQ-5D value sets and address preference heterogeneity. Both uncensored and censored data are supported. Furthermore, heteroscedasticity can be taken into account. It is possible to control for different covariates on the continuous and dichotomous data and start values can differ between the expected latent classes.

This package implements an efficient algorithm for fitting the entire regularization path of support vector machine models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

Bipartite graph-based hierarchical clustering, developed for pharmacogenomic datasets and datasets sharing the same data structure. The goal is to construct a hierarchical clustering of groups of samples based on association patterns between two sets of variables. In the context of pharmacogenomic datasets, the samples are cell lines, and the two sets of variables are typically expression levels and drug sensitivity values. For this method, sparse canonical correlation analysis from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> is first applied to extract association patterns for each group of samples. Then, a nuclear norm-based dissimilarity measure is used to construct a dissimilarity matrix between groups based on the extracted associations. Finally, hierarchical clustering is applied.