Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.

Encode and decode the Google Encoded Polyline Algorithm Format. See <https://developers.google.com/maps/documentation/utilities/polylinealgorithm> for more information.

Supports image files and graphic objects to be visualized in ggplot2 graphic system.

This package provides a simple wrapper for Wikipedia data. Specifically, this package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.

The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.

This package provides a quick and easy way of plotting the columns of two matrices or data frames against each other using ggplot2'. Although ggmatplot doesn't provide the same flexibility as ggplot2', it can be used as a workaround for having to wrangle wide format data into long format for plotting with ggplot2'.

GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks performance of a few standard linear algebra operations (such as a matrix product and QR, SVD and LU decompositions) across a number of different BLAS libraries as well as a GPU implementation. To do so, it takes advantage of the ability to plug and play different BLAS implementations easily on a Debian and/or Ubuntu system. The current version supports - Reference BLAS ('refblas') which are un-accelerated as a baseline - Atlas which are tuned but typically configure single-threaded - Atlas39 which are tuned and configured for multi-threaded mode - Goto Blas which are accelerated and multi-threaded - Intel MKL which is a commercial accelerated and multithreaded version. As for GPU computing, we use the CRAN package - gputools For Goto Blas', the gotoblas2-helper script from the ISM in Tokyo can be used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.

This package creates bar plots with rounded corners using ggplot2'. The code in this package was adapted from a solution provided by Stack Overflow user sthoch in the following post <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.

Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2025) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

We implemented multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024) <doi:10.1002/sim.10017>. Therefore, an asymptotic test, a groupwise bootstrap test, and a permutation test are incorporated with a Wald-type test statistic. The asymptotic and groupwise bootstrap test take the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMST contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.

Data from multi environment agronomic trials, which are often carried out by plant breeders, can be analyzed with the tools offered by this package such as the Additive Main effects and Multiplicative Interaction model or AMMI ('Gauch 1992, ISBN:9780444892409) and the Site Regression model or SREG ('Cornelius 1996, <doi:10.1201/9780367802226>). Since these methods present a poor performance under the presence of outliers and missing values, this package includes robust versions of the AMMI model ('Rodrigues 2016, <doi:10.1093/bioinformatics/btv533>), and also imputation techniques specifically developed for this kind of data ('Arciniegas-Alarcón 2014, <doi:10.2478/bile-2014-0006>).

Guild AI is an open-source tool for managing machine learning experiments. It's for scientists, engineers, and researchers who want to run scripts, compare results, measure progress, and automate machine learning workflow. Guild AI is a light weight, external tool that runs locally. It works with any framework, doesn't require any changes to your code, or access to any web services. Users can easily record experiment metadata, track model changes, manage experiment artifacts, tune hyperparameters, and share results. Guild AI combines features from Git', SQLite', and Make to provide a lab notebook for machine learning.

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).

This package provides ggplot2 extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.

Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_p,q regularization. Journal of Machine Learning Research, to appear, 2017".

Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

Download geyser eruption and observation data from the GeyserTimes site (<https://geysertimes.org>) and optionally store it locally. The vignette shows a simple analysis of downloading, accessing, and summarizing the data.

To create the multiple polygonal point layer for easily discernible shapes, we developed the package, it is like the geom_point of ggplot2'. It can be used to draw the scatter plot.

Works with ggplot2 to add a Van Gogh color palette to the userâ s repertoire. It also has a function that work alongside ggplot2 to create more interesting data visualizations and add contextual information to the userâ s plots.

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Connecting spatiotemporal exposure to individual and population-level risk via source-to-outcome continuum modeling. The package, methods, and case-studies are described in Messier, Reif, and Marvel (2025) <doi:10.1186/s40246-024-00711-8> and Eccles et al. (2023) <doi:10.1016/j.scitotenv.2022.158905>.

Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.

This package provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.

Scrapes Google Citation pages and creates data frames of citations over time.