Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.

Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.

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").

This package provides a dependency free interface to the H3 geospatial indexing system utilizing the Rust library h3o <https://github.com/HydroniumLabs/h3o> via the extendr library <https://github.com/extendr/extendr>.

Functions, data sets, analyses and examples from the second edition of the book A Handbook of Statistical Analyses Using R (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2008). The first chapter of the book, which is entitled An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available. In addition, the package contains Sweave code for producing slides for selected chapters (see HSAUR2/inst/slides).

This package provides the posterior estimates of the regression coefficients when horseshoe prior is specified. The regression models considered here are logistic model for binary response and log normal accelerated failure time model for right censored survival response. The linear model analysis is also available for completeness. All models provide deviance information criterion and widely applicable information criterion. See <doi:10.1111/rssc.12377> Maity et. al. (2019) <doi:10.1111/biom.13132> Maity et. al. (2020).

In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared. You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. HistDat allows for the calculation of summary statistics without the need for expanding your data.

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.

S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.

This package provides a fast, vectorized hashmap that is built on top of C++ std::unordered_map <https://en.cppreference.com/w/cpp/container/unordered_map.html>. The map can hold any R object as key / value as long as it is serializable and supports vectorized insertion, lookup, and deletion.

Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.

This package implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.

Statistical analysis of static chamber concentration data for trace gas flux estimation.

The hotspots package is designed to look within a set of measured values of a variable and identify values that are disproportionately high based on both the deviance of any given value from a statistical distribution and its similarity to other values. Because this relative magnitude of each value is taken into account, a value that is a statistical outlier may not always be a hot spot if other values are similarly large.

This package contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.

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>.

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).

Probability functions and other utilities for the generalized Hermite distribution.

Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016. Additional data sets containing geographical information and links to Wikipedia are also included.

An implementation of high-probability lower bounds for the total variance distance as introduced in Michel & Naef & Meinshausen (2020) <arXiv:2005.06006>. An estimated lower-bound (with high-probability) on the total variation distance between two probability distributions from which samples are observed can be obtained with the function HPLB.

Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).

This package provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile h3-js <https://github.com/uber/h3-js> and V8 <https://github.com/jeroen/v8>.

This package provides functions to calculate the Hotellingâ s T-squared statistic and corresponding confidence ellipses. Provides the semi-axes of the Hotellingâ s T-squared ellipses at 95% and 99% confidence levels. Enables users to obtain the coordinates in two or three dimensions at user-defined confidence levels, allowing for the construction of 2D or 3D ellipses with customized confidence levels. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.

This package implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.