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

This package provides a collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.

Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.

High throughput toxicokinetics ("HTTK") is the combination of 1) chemical-specific in vitro measurements or in silico predictions and 2) generic mathematical models, to predict absorption, distribution, metabolism, and excretion by the body. HTTK methods have been described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>). Here we provide examples (vignettes) applying HTTK to solve various problems in bioinformatics, toxicology, and exposure science. In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>), whenever a new HTTK model is developed, the code to generate the figures evaluating that model is added as a new vignettte.

This package provides functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <doi:10.48550/arXiv.2104.12597>, which will appear as <doi:10.1017/S0266466623000269>.

This data-only package was created for distributing data used in the examples of the hglm package.

The seed germination process starts with water uptake by the seed and ends with the protrusion of radicle and plumule under varying temperatures and soil water potential. Hydrotime is a way to describe the relationship between water potential and seed germination rates at germination percentages. One important quantity before applying hydrotime modeling of germination percentages is to consider the proportion of viable seeds that could germinate under saturated conditions. This package can be used to apply correction factors at various water potentials before estimating parameters like stress tolerance, and uniformity of the hydrotime model. Three different distributions namely, Gaussian, Logistic, and Extreme value distributions have been considered to fit the model to the seed germination time course. Details can be found in Bradford (2002) <https://www.jstor.org/stable/4046371>, and Bradford and Still(2004) <https://www.jstor.org/stable/23433495>.

This package implements Heckman selection models using a Bayesian approach via Stan and compares the performance of normal, Studentâ s t, and contaminated normal distributions in addressing complexities and selection bias (Heeju Lim, Victor E. Lachos, and Victor H. Lachos, Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo, 2025, under submission).

Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.

Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features, and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review <doi:10.1111/insr.70016>. This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of â The SMILE Project: Statistical Modelling and Inference to Live the Environmentâ , funded by the European Union â Next Generation EU (Punzo).

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.

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>.

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.

Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) <doi:10.1093/jxb/50.334.655> and Bradford (2002) <https://www.jstor.org/stable/4046371>.

Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.

Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", <https://depmix.github.io/hmmr/>.

The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.

Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) <doi:10.1111/biom.13722> and Gerard (2023b) <doi:10.1111/1755-0998.13856>.

This package implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature.

This package provides functions to view files in raw binary form like in a hex editor. Additional functions to specify and read arbitrary binary formats.

Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.

This package performs iterative extrapolation of species haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. HACSim outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.

Create compressed, interactive HTML (Hypertext Markup Language) reports with embedded Python code, custom JS ('JavaScript') and CSS (Cascading Style Sheets), and wrappers for CanvasXpress plots, networks and more. Based on <https://pypi.org/project/py-report-html/>, its sister project.

Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.