Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a hierarchical model and a mixture model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through benchmark models (independent and pooled). Some of the methods are discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

This package provides a visualization suite primarily designed for single-cell RNA-sequencing data analysis applications but well-suited for other purposes as well. It introduces novel plots to represent two-variable and frequency data and optimizes some commonly used plotting options (e.g., correlation, network, density, alluvial and volcano plots) for ease of usage and flexibility.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. To capture the benefit of all three ontologies, I developed HTGM3D', a three-dimensional version of GoMiner'.

This package provides a two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the â double robustnessâ property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.

LecÈ iile prof/cls trebuie completate cu un câmp "ora", astfel ca oricare douÄ lecÈ ii prof/cls/ora sÄ nu se suprapunÄ Ã®ntr-o aceeaÈ i orÄ . The prof/cls lessons must be completed with a "hour" field ('ora), so that any two prof/cls/ora lessons do not overlap in the same hour. <https://vlad.bazon.net/>.

Allows to detect spatial clusters of abnormal values on multivariate or functional data (Frévent et al. (2022) <doi:10.32614/RJ-2022-045>). See also: Frévent et al. (2023) <doi:10.1093/jrsssc/qlad017>, Smida et al. (2022) <doi:10.1016/j.csda.2021.107378>, Frévent et al. (2021) <doi:10.1016/j.spasta.2021.100550>. Cucala et al. (2019) <doi:10.1016/j.spasta.2018.10.002>, Cucala et al. (2017) <doi:10.1016/j.spasta.2017.06.001>, Jung and Cho (2015) <doi:10.1186/s12942-015-0024-6>, Kulldorff et al. (2009) <doi:10.1186/1476-072X-8-58>.

Fit, summarize and plot sinusoidal hysteretic processes using: two-step simple harmonic least squares, ellipse-specific non-linear least squares, the direct method, geometric least squares or linear least squares. See Yang, F and A. Parkhurst, "Efficient Estimation of Elliptical Hysteresis with Application to the Characterization of Heat Stress" <DOI:10.1007/s13253-015-0213-6>.

R interface for H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

This package provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.

This package provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2025) <doi:10.1080/07350015.2025.2526424>.

The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.

When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.

Collection of functions to help retrieving data from Hub'Eau the free and public French National APIs on water <https://hubeau.eaufrance.fr/>.

Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) <doi:10.1111/2041-210X.13345>).

Build better balance in causal inference models. halfmoon helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. halfmoon supports both weighting and matching techniques.

Antitrust analysis of healthcare markets. Contains functions to implement the semiparametric estimation technique described in Raval, Rosenbaum, and Tenn (2017) "A Semiparametric Discrete Choice Model: An Application to Hospital Mergers" <doi:10.1111/ecin.12454>.

Haplotype and covariate relative risks in case-control data are estimated by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights. French et al. (2006) <doi:10.1002/gepi.20161>.

This package provides a modern idiomatic header-only C++ interface for libhdf5'. Original software can be found at <https://github.com/highfive-devs/highfive/>.

Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.

S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.

The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. The Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to the number of lags involved in the test and therefore it should be used with extra caution. The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.

This package creates styled tables for data presentation. Export to HTML, LaTeX, RTF, Word', Excel', PowerPoint', typst', SVG and PNG. Simple, modern interface to manipulate borders, size, position, captions, colours, text styles and number formatting. Table cells can span multiple rows and/or columns. Includes a huxreg function to create regression tables, and quick_* one-liners to print tables to a new document.

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <https://jmlr.org/papers/v18/16-342.html>.