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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-gensphere 1.3
Propagated dependencies: r-sphericalcubature@1.5 r-simplicialcubature@1.3 r-rgl@1.3.31 r-mvmesh@1.6 r-geometry@0.5.2
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gensphere
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Spherical Distributions
Description:

Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.

r-ghypernet 1.1.2
Propagated dependencies: r-texreg@1.39.5 r-rootsolve@1.8.2.4 r-rlang@1.1.6 r-reshape2@1.4.5 r-purrr@1.2.0 r-plyr@1.8.9 r-pbmcapply@1.5.1 r-numbers@0.9-2 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://ghyper.net
Licenses: AGPL 3
Build system: r
Synopsis: Fit and Simulate Generalised Hypergeometric Ensembles of Graphs
Description:

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>.

r-geostan 0.8.2
Propagated dependencies: r-truncnorm@1.0-9 r-stanheaders@2.32.10 r-spdep@1.4-1 r-spdata@2.3.4 r-signs@0.1.2 r-sf@1.0-23 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-mass@7.3-65 r-gridextra@2.3 r-ggplot2@4.0.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://connordonegan.github.io/geostan/
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Spatial Analysis
Description:

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

r-gpom 1.4
Propagated dependencies: r-rgl@1.3.31 r-float@0.3-3 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GPoM
Licenses: FSDG-compatible
Build system: r
Synopsis: Generalized Polynomial Modelling
Description:

Platform dedicated to the Global Modelling technique. Its aim is to obtain ordinary differential equations of polynomial form directly from time series. It can be applied to single or multiple time series under various conditions of noise, time series lengths, sampling, etc. This platform is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects Crops'IChaos and Musc & SlowFast). The method is described in the article : Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.

r-ghcm 3.0.1
Propagated dependencies: r-rcpp@1.1.0 r-compquadform@1.4.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/arlundborg/ghcm
Licenses: Expat
Build system: r
Synopsis: Functional Conditional Independence Testing with the GHCM
Description:

This package provides a statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) <doi:10.1111/rssb.12544>.

r-gptstudio 0.4.0
Propagated dependencies: r-yaml@2.3.10 r-waiter@0.2.5-1.927501b r-stringr@1.6.0 r-sseparser@0.1.0 r-shiny-i18n@0.3.0 r-shiny@1.11.1 r-rvest@1.0.5 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-ids@1.0.1 r-httr2@1.2.1 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-glue@1.8.0 r-fontawesome@0.5.3 r-curl@7.0.0 r-colorspace@2.1-2 r-cli@3.6.5 r-bslib@0.9.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/MichelNivard/gptstudio
Licenses: Expat
Build system: r
Synopsis: Use Large Language Models Directly in your Development Environment
Description:

Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see <https://platform.openai.com/docs/introduction>.

r-grapes 1.0.0
Propagated dependencies: r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/wlandau/grapes
Licenses: GPL 3+
Build system: r
Synopsis: Make Binary Operators
Description:

Turn arbitrary functions into binary operators.

r-grobblr 0.2.2
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rcurl@1.98-1.17 r-purrr@1.2.0 r-png@0.1-8 r-magrittr@2.0.4 r-gridextra@2.3 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/calvinmfloyd/grobblR
Licenses: Expat
Build system: r
Synopsis: Creating Flexible, Reproducible 'PDF' Reports
Description:

This package provides a tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.

r-glm-predict 4.3-2
Propagated dependencies: r-vgam@1.1-13 r-survival@3.8-3 r-survey@4.4-8 r-nnet@7.3-20 r-mlogit@1.1-3 r-mass@7.3-65 r-lme4@1.1-37 r-dfidx@0.2-0 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/benjaminschlegel/glm.predict/
Licenses: GPL 2+
Build system: r
Synopsis: Predicted Values and Discrete Changes for Regression Models
Description:

This package provides functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], vglm() [generalized ordinal logistic model], multinom() [multinomial model], tobit() [tobit model], svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.

r-geohabnet 2.2
Propagated dependencies: r-yaml@2.3.10 r-viridislite@0.4.2 r-terra@1.8-86 r-stringr@1.6.0 r-rnaturalearth@1.1.0 r-patchwork@1.3.2 r-memoise@2.0.1 r-magrittr@2.0.4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-geosphere@1.5-20 r-future-apply@1.20.0 r-future@1.68.0 r-config@0.3.2 r-beepr@2.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://garrettlab.github.io/HabitatConnectivity/
Licenses: GPL 3
Build system: r
Synopsis: Geographical Risk Analysis Based on Habitat Connectivity
Description:

The geohabnet package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) <doi:10.1093/biosci/biaa067> proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in geohabnet were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of geohabnet are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in geohabnet to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the geohabnet package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the geohabnet package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) <doi:10.1029/2007gb002947> "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) <doi:10.7910/DVN/PRFF8V> "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the geohabnet package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The geohabnet package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The geohabnet package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in geohabnet provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as final outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at <https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html> to see examples of each function and how to access each of these outcome types. To change parameter values, the file called parameters.yaml stores the parameters and their values, can be accessed using get_parameters() and set new parameter values with set_parameters()'. Users can modify up to ten parameters.

r-grfa 0.2.2
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GrFA
Licenses: GPL 3+
Build system: r
Synopsis: Group Factor Analysis
Description:

This package implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.

r-gaussquad 1.0-3
Propagated dependencies: r-polynom@1.4-1 r-orthopolynom@1.0-6.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gaussquad
Licenses: GPL 2+
Build system: r
Synopsis: Collection of Functions for Gaussian Quadrature
Description:

This package provides a collection of functions to perform Gaussian quadrature with different weight functions corresponding to the orthogonal polynomials in package orthopolynom. Examples verify the orthogonality and inner products of the polynomials.

r-gwlelast 1.2.2
Propagated dependencies: r-spgwr@0.6-37 r-sp@2.2-0 r-glmnet@4.1-10 r-geosphere@1.5-20 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GWLelast
Licenses: Expat
Build system: r
Synopsis: Geographically Weighted Logistic Elastic Net Regression
Description:

Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.

r-gammafuncmodel 6.0
Propagated dependencies: r-scales@1.4.0 r-rootsolve@1.8.2.4 r-rlang@1.1.6 r-rdpack@2.6.4 r-patchwork@1.3.2 r-nlme@3.1-168 r-gridextra@2.3 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-dplyr@1.1.4 r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gammaFuncModel
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Non-Linear Mixed Effects Model Based on the Gamma Function Form
Description:

Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.

r-gghist 0.1.0
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/frederikziebell/gghist
Licenses: GPL 3+
Build system: r
Synopsis: Plot the Histogram of a Numeric Vector
Description:

Wrapper around geom_histogram() of ggplot2 to plot the histogram of a numeric vector. This is especially useful, since qplot() was deprecated in ggplot2 3.4.0.

r-gofkernel 2.1-3
Propagated dependencies: r-kernsmooth@2.23-26
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=GoFKernel
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Testing Goodness-of-Fit with the Kernel Density Estimator
Description:

Tests of goodness-of-fit based on a kernel smoothing of the data. References: Pavà a (2015) <doi:10.18637/jss.v066.c01>.

r-ggmugs 0.6.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-purrr@1.2.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=ggmugs
Licenses: Expat
Build system: r
Synopsis: Visualization of Multiple Genome-Wide Association Study Summary Statistics
Description:

This package provides a grammar of graphics approach for visualizing summary statistics from multiple Genome-wide Association Studies (GWAS). It offers geneticists, bioinformaticians, and researchers a powerful yet flexible tool for illustrating complex genetic associations using data from various GWAS datasets. The visualizations can be extensively customized, facilitating detailed comparative analysis across different genetic studies. Reference: Uffelmann, E. et al. (2021) <doi:10.1038/s43586-021-00056-9>.

r-gelnet 1.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=gelnet
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Elastic Nets
Description:

This package implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.

r-guilds 1.4.7
Propagated dependencies: r-rcpp@1.1.0 r-pracma@2.4.6 r-nloptr@2.2.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/thijsjanzen/GUILDS
Licenses: GPL 2
Build system: r
Synopsis: Implementation of Sampling Formulas for the Unified Neutral Model of Biodiversity and Biogeography, with or without Guild Structure
Description:

This package provides a collection of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and the guilds sampling formula conditioned on guild size (Janzen et al. 2015).

r-g-ridge 1.0
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=g.ridge
Licenses: GPL 2
Build system: r
Synopsis: Generalized Ridge Regression for Linear Models
Description:

Ridge regression due to Hoerl and Kennard (1970)<DOI:10.1080/00401706.1970.10488634> and generalized ridge regression due to Yang and Emura (2017)<DOI:10.1080/03610918.2016.1193195> with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

r-geosptdb 1.0-2
Propagated dependencies: r-statmatch@1.4.3 r-sp@2.2-0 r-minqa@1.2.8 r-mass@7.3-65 r-gsl@2.1-9 r-geospt@1.0-6 r-fields@17.1 r-fd@1.0-12.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://cran.r-project.org/package=geosptdb
Licenses: GPL 2+
Build system: r
Synopsis: Spatio-Temporal Radial Basis Functions with Distance-Based Methods (Optimization, Prediction and Cross Validation)
Description:

Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.

r-ggtrendline 1.0.3
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://github.com/PhDMeiwp/ggtrendline
Licenses: GPL 3
Build system: r
Synopsis: Add Trendline and Confidence Interval to 'ggplot'
Description:

Add trendline and confidence interval of linear or nonlinear regression model and show equation to ggplot as simple as possible. For a general overview of the methods used in this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.

r-goldfish 1.6.12
Propagated dependencies: r-tibble@3.3.0 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-generics@0.1.4 r-changepoint@2.3
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://stocnet.github.io/goldfish/
Licenses: GPL 3+
Build system: r
Synopsis: Statistical Network Models for Dynamic Network Data
Description:

This package provides tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

r-getmstatistic 0.2.2
Propagated dependencies: r-stargazer@5.2.3 r-psych@2.5.6 r-metafor@4.8-0 r-gtable@0.3.6 r-gridextra@2.3 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/g.scm (guix-cran packages g)
Home page: https://magosil86.github.io/getmstatistic/
Licenses: Expat
Build system: r
Synopsis: Quantifying Systematic Heterogeneity in Meta-Analysis
Description:

Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.

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