Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) <doi:10.1126/science.abd5085>.

This package provides a shiny application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended Holomics workflow. Methods used in the package were implemented in the package mixomics by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.

Test the significance of coefficients in high dimensional generalized linear models.

Plot an R package's recursive dependency graph and tabulate the number of unique downstream dependencies added by top-level dependencies. This helps R package developers identify which of their declared dependencies add the most downstream dependencies in order to prioritize them for removal if needed. Uses graph stress minimization adapted from Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.

Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, <doi:10.1073/pnas.79.8.2554>). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, <doi:10.48550/ARXIV.1606.01164>). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with OpenMP is used if available during compilation.

This package implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

This package contains most of the hex font files from the GNU Unifont Project <https://unifoundry.com/unifont/> compressed by xz'. GNU Unifont is a duospaced bitmap font that attempts to cover all the official Unicode glyphs plus several of the artificial scripts in the (Under-)ConScript Unicode Registry <https://www.kreativekorp.com/ucsur/>. Provides a convenience function for loading in several of them at the same time as a bittermelon bitmap font object for easy rendering of the glyphs in an R terminal or graphics device.

Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.

This package provides a Shiny app allowing to convert HTML code to R code (e.g. <span>Hello</span> to tags$span("Hello")'), for usage in a Shiny UI.

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.

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>.

Function to identify haplotypes within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP (Single Nucleotide Polymorphisms) within the QTL. This function groups together all individuals of a population with the same haplotype. Each group contains individual with the same allele in each SNP, whether or not missing data. Thus, haplotyper groups individuals, that to be imputed, have a non-zero probability of having the same alleles in the entire sequence of SNP's. Moreover, haplotyper calculates such probability from relative frequencies.

This package provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.

The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization constants of unnormalized probability distributions by utilizing holonomic systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method to find maximal likelihood estimates by utilizing the HGM.

An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015) <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>, cointegration analysis proposed by Zhang, Robinson and Yao (2019) <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022) <doi:10.1093/biomet/asab034>, white noise tests proposed by Chang, Yao and Zhou (2017) <doi:10.1093/biomet/asw066> and Chang et al. (2026+), CP-decomposition for matrix time series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and Chang et al. (2026+) <doi:10.48550/arXiv.2410.05634>, and statistical inference for spectral density matrix proposed by Chang et al. (2025) <doi:10.1080/01621459.2025.2468013>.

An interactive Shiny dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15â 24, 25â 34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly.

This package contains miscellaneous functions useful for managing NetCDF files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.

This package provides helper functions for analysing patient data in hyperthermic intraperitoneal chemotherapy (HIPEC) workflows. Includes functions to estimate peritoneal surface area (PSA), summarise registry data, and produce reporting graphics. Body surface area calculations are based on Du Bois and Du Bois (1916) <doi:10.1001/archinte.1916.00080130010002>.

The hydReng package provides a set of functions for hydraulic engineering tasks and natural hazard assessments. It includes basic hydraulics (wetted area, wetted perimeter, flow, flow velocity, flow depth, and maximum flow) for open channels with arbitrary geometry under uniform flow conditions. For structures such as circular pipes, weirs, and gates, the package includes calculations for pressure flow, backwater depth, and overflow over a weir crest. Additionally, it provides formulas for calculating bedload transport. The formulas used can be found in standard literature on hydraulics, such as Bollrich (2019, ISBN:978-3-410-29169-5) or Hager (2011, ISBN:978-3-642-77430-0).

Convenience date tools for identifying weekends, business days, and Canadian holidays, including R wrappers for the Canada Holidays API <https://canada-holidays.ca/>.

Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the bigmemory package.

This package provides a local haplotyping tool for use in trait association and trait prediction analyses pipelines. HaploVar enables users take single nucleotide polymorphisms (SNPs) (in VCF format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the output to be compatible with a wide range of trait association and trait prediction tools. The local haplotypes are calculated from the LD matrix using a clustering algorithm called density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) <ISBN: 1577350049>.

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>.

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>.