Fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.

Perform Hi-C data differential analysis based on pixel-level differential analysis and a post hoc inference strategy to quantify signal in clusters of pixels. Clusters of pixels are obtained through a connectivity-constrained two-dimensional hierarchical clustering.

Implementing Hierarchical Bayesian Small Area Estimation models using the brms package as the computational backend. The modeling framework follows the methodological foundations described in area-level models. This package is designed to facilitate a principled Bayesian workflow, enabling users to conduct prior predictive checks, model fitting, posterior predictive checks, model comparison, and sensitivity analysis in a coherent and reproducible manner. It supports flexible model specifications via brms and promotes transparency in model development, aligned with the recommendations of modern Bayesian data analysis practices, implementing methods described in Rao and Molina (2015) <doi:10.1002/9781118735855>.

In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.

An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2022).

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>).

Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) <doi:10.1093/bioadv/vbad038> and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.

This package provides a user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Lilin Yin et al. (2025) <doi:10.18637/jss.v114.i06>; Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.

This package provides a suite of routines for the hyperdirichlet distribution and reified Bradley-Terry; supersedes the hyperdirichlet package; uses disordR discipline <doi:10.48550/ARXIV.2210.03856>. To cite in publications please use Hankin 2017 <doi:10.32614/rj-2017-061>, and for Generalized Plackett-Luce likelihoods use Hankin 2024 <doi:10.18637/jss.v109.i08>.

Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.

Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the â small n, large pâ problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.

This package contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.

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.

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>.

Perform high dimensional Feature Selection in the presence of survival outcome. Based on Feature Selection method and different survival analysis, it will obtain the best markers with optimal threshold levels according to their effect on disease progression and produce the most consistent level according to those threshold values. The functions methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039> and Bhattacharjee et al (2021) <arXiv:2012.02102>.

State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective hydroPSO R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., TUWmodel', GR4J', GR6J'). However, the main focus of hydroMOPSO is optimising environmental and other real-world models that need to be run from the system console (e.g., SWAT+'). hydroMOPSO communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, hydroMOPSO can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, hydroMOPSO is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.

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>.

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.

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.

Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Allows to detect spatial clusters of abnormal values on multivariate or functional data (Frévent et al. (2022) <doi:10.32614/RJ-2022-045>). See also: Frévent et al. (2023) <doi:10.1093/jrsssc/qlad017>, Smida et al. (2022) <doi:10.1016/j.csda.2021.107378>, Frévent et al. (2021) <doi:10.1016/j.spasta.2021.100550>. Cucala et al. (2019) <doi:10.1016/j.spasta.2018.10.002>, Cucala et al. (2017) <doi:10.1016/j.spasta.2017.06.001>, Jung and Cho (2015) <doi:10.1186/s12942-015-0024-6>, Kulldorff et al. (2009) <doi:10.1186/1476-072X-8-58>.

We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. For a robust inference, we assume a Student's t error for these inputs. Given these inputs, the meta-analysis produces posterior samples of the model parameters including the Hubble constant via Metropolis-Hastings within Gibbs. The package provides an option to implement repelling-attracting Metropolis-Hastings within Gibbs in a case where the parameter space has multiple modes.

This package provides a Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. <doi:10.1198/106186004X2697>.

This package provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets with groups of variables with different degrees of variance, covariance, and effect size.