Probabilistic models describing the behavior of workload and queue on a High Performance Cluster and computing GRID under FIFO service discipline basing on modified Kiefer-Wolfowitz recursion. Also sample data for inter-arrival times, service times, number of cores per task and waiting times of HPC of Karelian Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011. Functions provided to import/export workload traces in Standard Workload Format (swf). Stability condition of the model may be verified either exactly, or approximately. Stability analysis: see Rumyantsev and Morozov (2017) <doi:10.1007/s10479-015-1917-2>, workload recursion: see Rumyantsev (2014) <doi:10.1109/PDCAT.2014.36>.

This package provides a number of utility functions useful for manipulating large histograms. This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning. It also provides a protocol buffer representation of R's native histogram class to allow histograms over large data sets to be computed and combined in distributed analytical pipelines. Implements bin-by-bin histogram distance measures described in Rubner, Tomasi and Guibas (2000) <doi:10.1023/A:1026543900054>, Swain and Ballard (1991) <doi:10.1007/BF00130487>, and Puzicha, Hofmann and Buhmann (1997) <doi:10.1109/CVPR.1997.609331>, and average shifted histograms as in Scott (2015, ISBN:9781118575536).

Several procedures for the hierarchical kernel extreme value process of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>, including simulation, estimation and spatial extrapolation. The spatial latent variable model <DOI:10.1214/11-STS376> is also included.

Audio interactivity within shiny applications using howler.js'. Enables the status of the audio player to be sent from the UI to the server, and events such as playing and pausing the audio can be triggered from the server.

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

Helping to calculate cricket specific problems in a tidy & simple manner.

Computes scalar performance metrics and goodness-of-fit summaries for comparing simulated and observed hydrological or regression values. Provides error metrics, percentage bias, Nash-Sutcliffe efficiency, Kling-Gupta efficiency variants, correlation measures, agreement indices, and comparison tables via gof() and gof_compare(). Metric definitions are registry-governed with shared validation, provenance-aware wrappers, and explicit handling of undefined or degenerate cases. Methods include Nash and Sutcliffe (1970) <doi:10.1016/0022-1694(70)90255-6>, Gupta et al. (2009) <doi:10.1016/j.jhydrol.2009.08.003>, Kling et al. (2012) <doi:10.1016/j.jhydrol.2012.01.011>, and Willmott et al. (2012) <doi:10.1002/joc.2419>.

This package provides a consistent API for hypothesis testing built on principles from Structure and Interpretation of Computer Programs': data abstraction, closure (combining tests yields tests), and higher-order functions (transforming tests). Implements z-tests, Wald tests, likelihood ratio tests, Fisher's method for combining p-values, and multiple testing corrections. Designed for use by other packages that want to wrap their hypothesis tests in a consistent interface.

Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:. Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>, Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.

This package provides functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.

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>.

Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at <https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) <doi:10.1080/00949650801935327>, while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) <doi:10.2307/2336685>. The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) <doi:10.1214/aos/1176350142>. This bootstrap scheme is known as weighted or wild bootstrap.

This package provides utility functions for, and drawing on, the data.table package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of utils and the author.

This package implements hierarchical Bayesian inference, robust frequentist inference, and distribution-free inference for dependent and unbalanced data under strong-mixing conditions. Supports triangular-array, weighted-sum and mixingale convergence regimes with Whittle and composite likelihoods, heteroskedasticity-and-autocorrelation-consistent variance estimation, block bootstrap with automatic block length, fixed-bandwidth HAR inference, adaptive conformal prediction, Bayesian decision under Region of Practical Equivalence, bridge-sampling Bayes factors, and predictive comparison via the Widely Applicable Information Criterion and leave-future-out cross-validation. Methods follow Andrews (1991) <doi:10.2307/2938229>, Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009) <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017) <doi:10.1007/s11222-016-9696-4>, Kruschke (2018) <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021) <doi:10.48550/arXiv.2106.00170>.

The heatex package calculates heat storage in the body and the components of heat exchange (conductive, convective, radiative, and evaporative) between the body and the environment during physical activity based on the principles of partitional calorimetry. The program enables heat exchange calculations for a range of environmental conditions when wearing various clothing ensembles.

This package provides a lightweight framework for building server-driven web applications in R'. htmxr combines the simplicity of htmx for partial page updates with the power of plumber2 for non-blocking HTTP endpoints. Build interactive dashboards and data applications without writing JavaScript', using familiar R patterns inspired by Shiny'. For more information on htmx', see <https://htmx.org>.

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.

This package provides a simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.

This package provides a data only package containing commercial domestic flights that departed Houston (IAH and HOU) in 2011.

Generates HIDECAN plots that summarise and combine the results of genome-wide association studies (GWAS) and transcriptomics differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot is presented in Angelin-Bonnet et al. (2023) (currently in review).

An open-source R package to deploys reproducible and flexible labels using layers. The huito package is part of the inkaverse project for developing different procedures and tools used in plant science and experimental designs. Learn more about the inkaverse project at <https://inkaverse.com/>.

The Hierarchical Neyman-Pearson (H-NP) classification framework extends the Neyman-Pearson classification paradigm to multi-class settings where classes have a natural priority ordering. This is particularly useful for classification in unbalanced dataset, for example, disease severity classification, where under-classification errors (misclassifying patients into less severe categories) are more consequential than other misclassifications. The package implements H-NP umbrella algorithms that controls under-classification errors under user specified control levels with high probability. It supports the creation of H-NP classifiers using scoring functions based on built-in classification methods (including logistic regression, support vector machines, and random forests), as well as user-trained scoring functions.

Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) <DOI:10.1139/cjfas-2017-0468>). hatchR offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.