Shiny-App that allows to annotate vectors of texts to predefined categories by hand.

Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling, in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>). The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).

Helper functions designed to make dynamically generating R Markdown documents easier by providing a simple and tidy way to create report pieces, shape them to your data, and combine them for exporting into a single R Markdown document.

Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.

This package implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in à ttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.

Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 <doi:10.1111/biom.12282>), (3)- regime switching regression model (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 <doi:10.1111/stan.12133>) (5)- robust emission model proposed by Qin et al, 2024 <doi:10.1007/s10479-024-05989-4> (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its arxiv version: <doi:10.48550/arXiv.2109.12489>).

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

Computes scalar performance metrics and goodness-of-fit summaries for comparing simulated and observed hydrological or regression values. Provides error metrics, percentage bias, Nash-Sutcliffe efficiency, Kling-Gupta efficiency variants, correlation measures, agreement indices, and comparison tables via gof() and gof_compare(). Metric definitions are registry-governed with shared validation, provenance-aware wrappers, and explicit handling of undefined or degenerate cases. Methods include Nash and Sutcliffe (1970) <doi:10.1016/0022-1694(70)90255-6>, Gupta et al. (2009) <doi:10.1016/j.jhydrol.2009.08.003>, Kling et al. (2012) <doi:10.1016/j.jhydrol.2012.01.011>, and Willmott et al. (2012) <doi:10.1002/joc.2419>.

This package performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection.

This package provides a unified, flexible framework for high dimensional feature selection in the presence of a survival outcome. Provides multiple machine learning approaches (Cox elastic net, random survival forest, accelerated oblique random survival forest, gradient-boosted Cox, stability selection, classical univariate Cox screening, pseudo- observation bridging to arbitrary regression learners, and Fine-Gray competing risks selection) under a single interface. Adds causal survival forest estimation of heterogeneous treatment effects on survival (experimental), conformal survival prediction with finite- sample coverage guarantees, and time-dependent SHAP explanations via SurvSHAP(t)'. Methodology is based on regularised Cox regression (2011) <doi:10.18637/jss.v039.i05>, random survival forests (2008) <doi:10.1214/08-AOAS169>, oblique random survival forests (2024) <doi:10.1080/10618600.2023.2231048>, stability selection (2010) <doi:10.1111/j.1467-9868.2010.00740.x>, causal survival forests (2023) <doi:10.1111/rssb.12538>, time-dependent survival explanations (2023) <doi:10.1016/j.knosys.2022.110234>, conformal survival prediction (2023) <doi:10.1093/biomet/asad043>, the Fine-Gray model for competing risks (1999) <doi:10.1080/01621459.1999.10474144>, and pseudo-observation regression (2010) <doi:10.1177/0962280209105020>.

These sample data sets are intended for historians learning R. They include population, institutional, religious, military, and prosopographical data suitable for mapping, quantitative analysis, and network analysis.

This package provides a tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, regional linkage) to hierarchical clustering in R ('hclust function in stats library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.

Calculate and visualize Healthy Eating Index (HEI) scores from National Health and Nutrition Examination Survey 24-hour dietary recall data utilizing three methods recommended by the National Cancer Institute (2024) <https://epi.grants.cancer.gov/hei/hei-methods-and-calculations.html#:~:text=To%20use%20the%20simple%20HEI,the%20total%20scores%20across%20individuals.>. Effortlessly analyze HEI scores across different demographic groups and years.

Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.

Statistical functions used in the French HydroPortail <https://hydro.eaufrance.fr/>. This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.

This package provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.

This package provides constructors for hl-rhotrices of dimension 2, 4 and 6, together with computation of the determinant, adjoint, inverse and eigenvalues under the Robust Multiplication Method (RMM). A ggplot2'-based function visualises the rhomboidal layout and the decomposition into principal minors.

Harriet was Charles Darwin's pet tortoise (possibly). harrietr implements some function to manipulate distance matrices and phylogenetic trees to make it easier to plot with ggplot2 and to manipulate using tidyverse tools.

This package provides tools for emitting the Problem Details structure defined in RFC 7807 <https://tools.ietf.org/html/rfc7807> for reporting errors from HTTP servers in a standard way.

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'.

Datasets related to Hong Kong, including information on the 2019 elected District Councillors (<https://www.districtcouncils.gov.hk> and <https://dce2019.hk01.com/>) and traffic collision data from the Hong Kong Department of Transport (<https://www.td.gov.hk/>). All of the data in this package is available in the public domain.

Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individualâ s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 <doi:10.1016/j.socscimed.2009.05.013>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 <doi:10.1017/S0003055403000881>; Jurges 2007 <doi:10.1002/hec.1134>; Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 <doi:10.1016/j.socscimed.2019.03.002>). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior.

Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, hdMTD includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.

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.