Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.

This package provides tools for processing heatmap images, removing backgrounds, calculating automatic arena calibration, and extracting behavioral metrics such as thigmotaxis index, Gini behavioral coefficient, and center of mass.

This package provides a novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.

We extend Adversarial Random Forests to a high-dimensional framework. The method partitions the feature space into regions where the assumption of feature independence within tree leaves is more likely to hold. Region-specific adversarial random forest models are trained to capture local dependence structures, while an additional adversarial random forest is fitted to a meta-space representation to model dependencies between regions. New observations are generated by first sampling from the meta-space model and then conditionally sampling from each region-specific model. The proposed methodology is described in Fouodo et al. (2026) <doi:10.64898/2026.09.09.750490>.

This package implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures.

The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. For details on the original version of the model written in Borland Pascal, see: Boose, Chamberlin, and Foster (2001) <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2> and Boose, Serrano, and Foster (2004) <doi:10.1890/02-4057>.

An interactive HTML widget of the perspective plot for generalized additive models; an alternative solution of the function mgcv::vis.gam(). This R package author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf182> and <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.3.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

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/>.

This package implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.

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.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Work with model files (setup, input, output) from the hydrological catchment model HYPE: Streamlined file import and export, standard evaluation plot routines, diverse post-processing and aggregation routines for hydrological model analysis. The HYPEtools package is also archived at <doi:10.5281/zenodo.7627955> and can be cited in publications with Brendel et al. (2024) <doi:10.1016/j.envsoft.2024.106094>.

Maintenance has been discontinued for this package. It has been superseded by GeneralizedHyperbolic'. GeneralizedHyperbolic includes all the functionality of HyperbolicDist and more and is based on a more rational design. HyperbolicDist provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data.

Allows users to create high-quality heatmaps from labelled, hierarchical data. Specifically, for data with a two-level hierarchical structure, it will produce a heatmap where each row and column represents a category at the lower level. These rows and columns are then grouped by the higher-level group each category belongs to, with the names for each category and groups shown in the margins. While other packages (e.g. dendextend') allow heatmap rows and columns to be arranged by groups only, hhmR also allows the labelling of the data at both the category and group level.

This package creates self-contained widgets for interactive phylogenetic tree visualization. This package wraps the JavaScript heat-tree package using the htmlwidgets R package.

This package provides a suite of functions to ping URLs and to time HTTP requests'. Designed to work with httr'.

HIGHT(HIGh security and light weigHT) algorithm is a block cipher encryption algorithm developed to provide confidentiality in computing environments that demand low power consumption and lightweight, such as RFID(Radio-Frequency Identification) and USN(Ubiquitous Sensor Network), or in mobile environments that require low power consumption and lightweight, such as smartphones and smart cards. Additionally, it is designed with a simple structure that enables it to be used with basic arithmetic operations, XOR, and circular shifts in 8-bit units. This algorithm was designed to consider both safety and efficiency in a very simple structure suitable for limited environments, compared to the former 128-bit encryption algorithm SEED. In December 2010, it became an ISO(International Organization for Standardization) standard. The detailed procedure is described in Hong et al. (2006) <doi:10.1007/11894063_4>.

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.

Inference approach for jointly modeling correlated count and binary outcomes. This formulation allows simultaneous modeling of zero inflation via the Bernoulli component while providing a more accurate assessment of the Hierarchical Zero-Inflated Poisson's parsimony (Lizandra C. Fabio, Jalmar M. F. Carrasco, Victor H. Lachos and Ming-Hui Chen, Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros, 2026, under submission).

This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. We implement an effective solution to recover the above hidden compact representation under the likelihood framework. Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.

Estimates body size and biomass of organisms from allometric relationships. It works with any data that follow a few column-naming conventions, using the measurements you supply to impute the ones that are missing.

The different methods for defining, detecting, and categorising the extreme events known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air and water temperature data of hourly and daily temporal resolution. These detection algorithms may be used on non-temperature data as well.

This package provides a bootstrapper that launches a command-line coding agent of the user's choice in a terminal tab pre-configured for a professional R role. Each role is described by a curated harness: a subset of community skills, a system prompt, a folder layout, and quality gates. The package does not run an agent loop and does not call a language model; it discovers the chosen coder binary, generates its configuration, links the curated skills, and opens the terminal. Code written by the agent is run manually by the user, by design, so that every generated script passes through a human audit gate before execution.

This package provides utilities for runtime hotpatching of locked R package namespaces. The package enables dynamic injection of function patches into sealed package environments without rebuilding or redeploying the package. This is particularly useful for legacy containerized workflows where package versions are frozen in place. The core functionality includes inject_patch() to inject patches into package namespaces, undo_patch() to restore original functions, apply_hotfix_file() to apply patches from external R scripts, and test_patched_dir() to run test suites against patched packages. The package implements namespace surgery techniques that allow internal callers to automatically see patched functions.