This package provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

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.

Harmony is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try Harmony at <https://harmonydata.ac.uk/app/> and you can read our blog at <https://harmonydata.ac.uk/blog/> or at <https://fastdatascience.com/how-does-harmony-work/>. Documentation at <https://harmonydata.ac.uk/harmony-r-released/>.

Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) <doi:10.1016/j.cageo.2008.08.011>. The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves.

Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) <DOI:10.1029/2007WR006716>.

This package provides a correlation-based batch process for fast, accurate imputation for high dimensional missing data problems via chained random forests. See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on hdImpute', Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on missForest', and Mayer (2022) <https://github.com/mayer79/missRanger> for more on missRanger'.

Cellular responses to perturbations are highly heterogeneous and depend largely on the initial state of cells. Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see Berg et al <doi:10.1101/2024.10.28.620481>). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results.

This package provides a shiny application, which allows you to perform single- and multi-omics analyses using your own omics datasets. After the upload of the omics datasets and a metadata file, single-omics is performed for feature selection and dataset reduction. These datasets are used for pairwise- and multi-omics analyses, where automatic tuning is done to identify correlations between the datasets - the end goal of the recommended Holomics workflow. Methods used in the package were implemented in the package mixomics by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752> and are described there in further detail.

Compute house price indexes and series using a variety of different methods and models common through the real estate literature. Evaluate index goodness based on accuracy, volatility and revision statistics. Background on basic model construction for repeat sales models can be found at: Case and Quigley (1991) <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.

Collection of functions to help retrieving data from Hub'Eau the free and public French National APIs on water <https://hubeau.eaufrance.fr/>.

These sample data sets are intended for historians learning R. They include population, institutional, religious, military, and prosopographical data suitable for mapping, quantitative analysis, and network analysis.

This package provides a streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting HLAMatchMaker <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.

Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).

Hedgehog will eat all your bugs. Hedgehog is a property-based testing package in the spirit of QuickCheck'. With Hedgehog', one can test properties of their programs against randomly generated input, providing far superior test coverage compared to unit testing. One of the key benefits of Hedgehog is integrated shrinking of counterexamples, which allows one to quickly find the cause of bugs, given salient examples when incorrect behaviour occurs.

This package provides a two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the â double robustnessâ property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.

We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faithâ s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package.

This package provides two functions that implement the one-sided and two-sided versions of the Hodrick-Prescott filter. The one-sided version is a Kalman filter-based implementation, whereas the two- sided version uses sparse matrices for improved efficiency. References: Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682> Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x> Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html> For more references, see the vignette.

An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>. A hero is a specific type of sandwich. Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) <doi:10.1016/j.spasta.2020.100413>.

For supersonic aircraft, flying subsonic over land, find the best route between airports. Allow for coastal buffer and potentially closed regions. Use a minimal model of aircraft performance: the focus is on time saved versus subsonic flight, rather than on vertical flight profile. For modelling and forecasting, not for planning your flight!

The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.

Allows users to create time series of tropical storm exposure histories for chosen counties for a number of hazard metrics (wind, rain, distance from the storm, etc.). This package interacts with data available through the hurricaneexposuredata package, which is available in a drat repository. To access this data package, see the instructions at <https://github.com/geanders/hurricaneexposure>. The size of the hurricaneexposuredata package is approximately 20 MB. This work was supported in part by grants from the National Institute of Environmental Health Sciences (R00ES022631), the National Science Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program Grant (NNX09AV81G).

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <https://jmlr.org/papers/v18/16-342.html>.

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>.

Creating effective colour palettes for figures is challenging. This package generates and plot palettes of optimally distinct colours in perceptually uniform colour space, based on iwanthue <http://tools.medialab.sciences-po.fr/iwanthue/>. This is done through k-means clustering of CIE Lab colour space, according to user-selected constraints on hue, chroma, and lightness.