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r-synchronicity
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public |
Boost Mutex Functionality in R
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2026-03-27 |
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r-forestploter
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public |
Create a Flexible Forest Plot
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2026-03-27 |
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r-btw
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public |
A Toolkit for Connecting R and Large Language Models
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2026-01-06 |
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r-mcptools
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public |
Model Context Protocol Servers and Clients
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2026-01-06 |
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shinyswatch
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public |
Bootswatch + Bootstrap 5 themes for Shiny.
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2025-04-25 |
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r-clusterr
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public |
Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids
and Affinity Propagation Clustering
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2025-03-25 |
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r-tidyclust
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public |
A Common API to Clustering
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2025-03-25 |
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r-referenceintervals
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public |
Reference Intervals
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2025-03-25 |
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r-gamlss.add
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public |
Interface for extra smooth functions including tensor products, neural networks and decision trees.
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2025-03-25 |
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r-r-reflimr
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public |
Uses an indirect method based on truncated quantile-quantile plots to estimate reference limits from routine laboratory data. The principle of the method was developed by Robert G Hoffmann (1963) <doi:10.1001/jama.1963.03060110068020> and modified by Georg Hoffmann and colleagues (2015) <doi:10.1515/labmed-2015-0104>, (2020) <doi:10.1515/labmed-2020-0005>, and (2022) <doi:10.1007/978-3-031-15509-3_31>.
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2025-03-25 |
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r-dalextra
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public |
Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot.
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2025-03-25 |
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r-uwot
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public |
An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370> is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<https://github.com/jlmelville/uwot>) for more documentation and examples.
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2025-03-25 |
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r-mvmr
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public |
An R package for performing multivariable Mendelian randomization analyses.
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2025-03-25 |
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r-twosamplemr
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public |
A package for performing Mendelian randomization using GWAS summary data. It uses the IEU GWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis. You can use the MR-Base web app <https://www.mrbase.org/> to try out a limited range of the functionality in this package, but for any serious work we strongly recommend using this R package.
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2025-03-25 |
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r-ieugwasr
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public |
Interface to the 'OpenGWAS' database API <https://gwas-api.mrcieu.ac.uk/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.
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2025-03-25 |
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r-mrpresso
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public |
MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a framework that allows for the evaluation of pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics.
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2025-03-25 |
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r-mrmix
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public |
This package gives robust estimation of causal effects by conducting Mendelian randomization analysis using a mixture model approach.
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2025-03-25 |
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r-radialmr
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public |
Non-linear Mendelian randomization analysis to investigate the shape of exposure-outcome relationships.
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2025-03-25 |
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r-mrinstruments
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public |
Non-linear Mendelian randomization analysis to investigate the shape of exposure-outcome relationships.
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2025-03-25 |
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r-mr.raps
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public |
Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
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2025-03-25 |
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r-nlmr
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public |
Non-linear Mendelian randomization analysis to investigate the shape of exposure-outcome relationships.
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2025-03-25 |
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r-gamlss.ggplots
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public |
Functions for plotting Generalized Additive Models for Location Scale and Shape from the 'gamlss' package, Stasinopoulos and Rigby (2007) <doi:10.18637/jss.v023.i07>, using the graphical methods from 'ggplot2'.
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2025-03-25 |
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r-gamlss.foreach
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public |
Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.
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2025-03-25 |
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r-gamlss.inf
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public |
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
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2025-03-25 |
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r-gamlss.dist
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public |
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
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2025-03-25 |
