Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).

An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by DÃ az and van der Laan (2011) <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, DÃ az, and van der Laan <doi:10.48550/arXiv.2205.05777>).

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.

This package provides a toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA GitHub repository (ANHIG/IMGTHLA) <https://github.com/ANHIG/IMGTHLA>, validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods.

This package provides a handy collection of utility functions designed to aid in package development, plotting and scientific research. Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions.

The HistData package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.

Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.

This package provides a collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>), Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.

This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. Selection of relevant references (in chronological order): WHO (2003) <https://www.who.int/publications/i/item/9241546204>, Murray et al. (2003) <doi:10.1186/1478-7954-1-1>, Miller & Hurley (2003) <doi:10.1136/jech.57.3.200>, Steenland & Armstrong (2006) <doi:10.1097/01.ede.0000229155.05644.43>, WHO (2011) <https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27>, GBD 2019 Risk Factors Collaborators (2020) <doi:10.1016/S0140-6736(20)30752-2>.

Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) <doi:10.1016/j.cageo.2008.08.011>. The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves.

This package provides a wrapper for the Highcharts library including shortcut functions to plot R objects. Highcharts <https://www.highcharts.com/> is a charting library offering numerous chart types with a simple configuration syntax.

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>.

This package provides a multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package HDMT we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) <doi:10.1080/01621459.2020.1765785>.

Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) <doi:10.1002/cjs.11534>.

Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).

Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) <doi:10.48550/arXiv.2306.04702> , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) <doi:10.1111/rssb.12243>, and the method of Pilliat et al (2023) <doi:10.1214/23-EJS2126>.

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 a user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient HDF5 types, removing the need to manage low-level details like dataspaces or property lists. Uses the HDF5 library developed by The HDF Group <https://www.hdfgroup.org/>.

This package provides a method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula.

This package provides functions for basic hydraulic calculations related to water flow in circular pipes both flowing full (under pressure), and partially full (gravity flow), and trapezoidal open channels. For pressure flow this includes friction loss calculations by solving the Darcy-Weisbach equation for head loss, flow or diameter, plotting a Moody diagram, matching a pump characteristic curve to a system curve, and solving for flows in a pipe network using the Hardy-Cross method. The Darcy-Weisbach friction factor is calculated using the Colebrook (or Colebrook-White equation), the basis of the Moody diagram, the original citation being Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the Manning equation is used, again solving for missing parameters. The derivation of and solutions using the Darcy-Weisbach equation and the Manning equation are outlined in many fluid mechanics texts such as Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and rapidly-varied flow functions are included. For the Manning equation solutions, this package uses modifications of original code from the iemisc package by Irucka Embry.

This package provides a set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").

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. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of High-Throughput GoMiner ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair.

This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).

This package provides a way to display word clouds in R. The word cloud is a html widget, so you can use it in interactive documents and shiny applications.