By binding R functions and the Highmaps <https://www.highcharts.com.cn/products/highmaps> chart library, hchinamap package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.

This package provides a novel searching scheme for tuning parameter in high-dimensional penalized regression. We propose a new estimate of the regularization parameter based on an estimated lower bound of the proportion of false null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>). The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance testing, which is constructed by dependent p-values from a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate of tuning parameter in penalized regression is decided corresponding to the lower bound of the proportion of false null hypotheses. Different penalized regression methods are provided in the multi-split algorithm.

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).

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.

Miscellaneous convenience functions and wrapper functions to convert frequencies between Hz, semitones, mel and Bark, to create a matrix of dummy columns from a factor, to determine whether x lies in range [a,b], and to add a bracketed line to an existing plot. This package also contains an example data set of a stratified sample of 80 talkers of Dutch.

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 performs multiple hot-deck imputation of categorical and continuous variables in a data frame.

Allows users to create high-quality heatmaps from labelled, hierarchical data. Specifically, for data with a two-level hierarchical structure, it will produce a heatmap where each row and column represents a category at the lower level. These rows and columns are then grouped by the higher-level group each category belongs to, with the names for each category and groups shown in the margins. While other packages (e.g. dendextend') allow heatmap rows and columns to be arranged by groups only, hhmR also allows the labelling of the data at both the category and group level.

The conditional treatment effect for competing risks data in observational studies is estimated. While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) <arXiv:2112.09535>.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.

Pfafstetter Hydrological Codes as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> are decoded for upstream or downstream queries.

Conducts analyses for healthcare program evaluations or intervention studies. Calculates regression analyses for standard ordinary least squares (OLS or linear) or logistic models. Performs regression models used for causal modeling such as differences-in-differences (DID) and interrupted time series (ITS) models. Provides limited interpretations of model results and a ranking of variable importance in models. Performs propensity score models, top-coding of model outcome variables, and can return new data with the newly formed variables. Also performs Cronbach's alpha for various scale items (e.g., survey questions). See Github URL for examples in the README file. For more details on the statistical methods, see Allen & Yen (1979, ISBN:0-8185-0283-5), Angrist & Pischke (2009, ISBN:9780691120355), Harrell (2016, ISBN:978-3-319-19424-0), Kline (1999, ISBN:9780415211581), Linden (2015) <doi:10.1177/1536867X1501500208>, Merlo (2006) <doi:10.1136/jech.2004.029454> Muthen & Satorra (1995) <doi:10.2307/271070>, and Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5).

Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms outputs to gating strategies outputs.

Deprecated.

Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. <doi:10.1371/journal.pone.0168583>, and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. <doi:10.1371/journal.pone.0168675>. A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, <doi:10.1007/978-3-319-75268-6>, ISBN 978-3-319-75267-9.

This package provides a procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve.

This package provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling users to query, download, and process data efficiently from the HDA platform. With hdar', researchers and data scientists can integrate the extensive HDA datasets into their R workflows, enhancing their data analysis capabilities. Comprehensive information on the API functionality and usage is available at <https://gateway.prod.wekeo2.eu/hda-broker/docs>.

This package provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points. More details are available in Parast et al (2024) <doi:10.1002/sim.10122>.

Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package hiphop can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance.

Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

This package contains the National Health and Nutrition Examination Survey 24-hour dietary recall data and Healthy Eating Index scoring standards used by the heiscore package.

This package provides a set of tools to create georeferenced hillshade relief raster maps using ray-tracing and other advanced hill-shading techniques. It includes a wrapper function to create a georeferenced, ray-traced hillshade map from a digital elevation model, and other functions that can be used in a rayshader pipeline.

Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis. The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.