Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad et al. (2019) <doi:10.1016/j.jml.2019.104038> and Rabe et al. (2020) <doi:10.21105/joss.02134>.

Set of tools to help interested researchers to build hospital networks from data on hospitalized patients transferred between hospitals. Methods provided have been used in Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715>, and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) <doi:10.1038/s41598-020-71212-6>.

An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by DÃ az and van der Laan (2011) <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, DÃ az, and van der Laan <doi:10.48550/arXiv.2205.05777>).

Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure.

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.

In order to improve performance for HTTP API clients, httpcache provides simple tools for caching and invalidating cache. It includes the HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in replacements for those in the httr package. These functions are cache-aware and provide default settings for cache invalidation suitable for RESTful APIs; the package also enables custom cache-management strategies. Finally, httpcache includes a basic logging framework to facilitate the measurement of HTTP request time and cache performance.

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/>.

Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.

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>.

Create publication-quality, 2-dimensional visualizations of alpha-helical peptide sequences. Specifically, allows the user to programmatically generate helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of alpha-helical oligopeptides. See Wadhwa RR, et al. (2018) <doi:10.21105/joss.01008> for more information.

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.

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

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 a set of routines to quickly download and import the HUGO Gene Nomenclature Committee (HGNC) data set on mapping of gene symbols to gene entries in other genomic databases or resources.

This package provides a broad collection of datasets focused on health, biomechanics, and human motion. It includes clinical, physiological, and kinematic information from diverse sources, covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis, accelerometry, gait analysis, motion sensing, and biomechanics experiments. Designed for researchers, analysts, and students, the package facilitates exploration and analysis of data related to health monitoring, physical activity, and rehabilitation.

Error type I and Optimal critical values to test statistical hypothesis based on Neyman-Pearson Lemma and Likelihood ratio test based on random samples from several distributions. The families of distributions are Bernoulli, Exponential, Geometric, Inverse Normal, Normal, Gamma, Gumbel, Lognormal, Poisson, and Weibull. This package is an ideal resource to help with the teaching of Statistics. The main references for this package are Casella G. and Berger R. (2003,ISBN:0-534-24312-6 , "Statistical Inference. Second Edition", Duxbury Press) and Hogg, R., McKean, J., and Craig, A. (2019,ISBN:013468699, "Introduction to Mathematical Statistic. Eighth edition", Pearson).

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.

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.

This package provides a shiny interface for a free, open-source managerial accounting-like system for health care practices. This package allows health care administrators to project revenue with monthly adjustments and procedure-specific boosts up to a 3-year period. Granular data (patient-level) to aggregated data (department- or hospital-level) can all be used as valid inputs provided historical volume and revenue data is available. For more details on managerial accounting techniques, see Brewer et al. (2015, ISBN:9780078025792).

This package provides a set of objects and functions for Bayes Linear emulation and history matching. Core functionality includes automated training of emulators to data, diagnostic functions to ensure suitability, and a variety of proposal methods for generating waves of points. For details on the mathematical background, there are many papers available on the topic (see references attached to function help files or the below references); for details of the functions in this package, consult the manual or help files. Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>. Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>. Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.

This package creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned. The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/). Keywords: networks, food webs, linnet, systems biology, bioinformatics.

This package provides tools for species distribution modeling using H3 hexagonal grids (Uber Technologies Inc., 2022, <https://h3geo.org>). Facilitates retrieval of species occurrence records, generation of H3 grids, computation of landscape metrics, and preparation of spatial data for modern species distribution models workflows. Designed for biodiversity and landscape ecology research.

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