This is a companion to Henry-Stewart talk by Zhao (2026, <doi:10.69645/FRFQ9519>), which gathers information, metadata and scripts to showcase modern genetic analysis -- ranging from testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with traits using genetic and statistical models, Bayesian implementation, power calculation in study design, and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. The earlier version by Zhao (2009, <doi:10.69645/DCRY5578>) provides an brief introduction to these topics.

Datasets used in the book Graphical Data Analysis with R (Antony Unwin, CRC Press 2015).

This package contains the development of a tool that provides a web-based graphical user interface (GUI) to perform Techniques from a subset of spatial statistics known as geographically weighted (GW) models. Contains methods described by Brunsdon et al., 1996 <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002 <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011 <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007 <doi:10.1111/j.1538-4632.2007.00709.x>.

The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them.

Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.

This package provides functions for performing graphical difference testing. Differences are generated between raster images. Comparisons can be performed between different package versions and between different R versions.

This package provides a simple way to translate text elements in ggplot2 plots using a dictionary-based approach.

The GB2 package explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distribution are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.

Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).

We implemented multiple tests based on the restricted mean time lost (RMTL) for general factorial designs as described in Munko et al. (2024) <doi:10.48550/arXiv.2409.07917>. Therefore, an asymptotic test and a permutation test are incorporated with a Wald-type test statistic. The asymptotic test takes the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMTL contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.

The geomod does spatial prediction of the Geotechnical soil properties. It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a map or a series of maps. It uses the interpolation technique where a single or statistically â bestâ estimate of spatial occurrence soil property is determined. The interpolation is based on both the sampled data and a variogram model for the spatial correlation of the sampled data. The single estimate is produced by a Kriging technique.

This package provides a collection of several geoms to create graphics, using ggplot2 and the Cartesian coordinate system. You use the familiar mapping Grammar of Graphics without the need to do another transformation into polar coordinates.

This package provides a user-friendly, highly customizable R package for building horizon plots in the ggplot2 environment.

The American Association Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma Collaborative represents a multi-year, multi-institution effort to build a pan-cancer repository of linked clinico-genomic data. The genomic and clinical data are provided in multiple releases (separate releases for each cancer cohort with updates following data corrections), which are stored on the data sharing platform Synapse <https://www.synapse.org/>. The genieBPC package provides a seamless way to obtain the data corresponding to each release from Synapse and to prepare datasets for analysis.

Process open standard GPX files into data.frames for further use and analysis in R.

This package provides functions and necessary JavaScript bindings to quickly transfer spatial data from R memory or remote URLs to the browser for use in interactive HTML widgets created with the htmlwidgets R package. Leverages GeoArrow (<https://geoarrow.org/>) data representation for data stored in local R memory which is generally faster than traditional GeoJSON by minimising the amount of copy, serialization and deserialization steps necessary for the data transfer. Furthermore, provides functionality and JavaScript bindings to consume GeoParquet (<https://geoparquet.org/>) files from remote URLs in the browser.

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

This package provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <doi:10.48550/arXiv.1607.02441>. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>. Casiraghi, G., (2017) <doi:10.48550/arXiv.1702.02048>. Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>. Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>. Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>. Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.

This package contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/groupICA/>.

This package provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The implementation is in C++ and uses RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included.

This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms.

Plot glycans following the Symbol Nomenclature for Glycans (SNFG) using ggplot2'. SNFG provides a standardized visual representation of glycan structures.