Algebra of operations for blending, copying, adjusting, and compositing layers in ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.

The first major functionality is to compute the bias in regression coefficients of misspecified linear gene-environment interaction models. The most generalized function for this objective is GE_bias(). However GE_bias() requires specification of many higher order moments of covariates in the model. If users are unsure about how to calculate/estimate these higher order moments, it may be easier to use GE_bias_normal_squaredmis(). This function places many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate normal distribution) and is thus able to automatically calculate many of the higher order moments automatically, necessitating only that the user specify some covariances. There are also functions to solve for the bias through simulation and non-linear equation solvers; these can be used to check your work. Second major functionality is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample performance than other inference procedures for testing GxE interaction. More details on these functions are available in Sun, Carroll, Christiani, and Lin (2018) <doi:10.1111/biom.12813>.

Interface for the GitHub API that enables efficient management of courses on GitHub. It has a functionality for managing organizations, teams, repositories, and users on GitHub and helps automate most of the tedious and repetitive tasks around creating and distributing assignments.

This package provides an interface to the GeoNode API, allowing to upload and publish metadata and data in GeoNode'. For more information about the GeoNode API, see <https://geonode.org/>.

Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.

Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.

This package provides tools to compute the Generalized Measure of Correlation (GMC), a dependence measure accounting for nonlinearity and asymmetry in the relationship between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.

Load polar volume and vertical profile data for aeroecological research directly into R. With getRad you can access data from several sources in Europe and the US and standardize it to facilitate further exploration in tools such as bioRad'.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

This package implements the generalized propensity score cumulative distribution function proposed by Greene (2017) <https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>. A single scalar balancing score is calculated for any generalized propensity score vector with three or more treatments. This balancing score is used for propensity score matching and stratification in outcome analyses when analyzing either ordinal or multinomial treatments.

This package provides a nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.

Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.

D&D alignment charts show 9 boxes with values for good through evil and values for chaotic through lawful. This package easily creates these alignment charts from user-provided image paths and alignment values.

In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.

This package provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive RStudio plot for better guessing dynamically of initial values for ease of included optimization and simulating.

Allows you to write queries that combine SQL (Structured Query Language) data retrieval with visualization specifications in a single, composable syntax. The ggsql package binds directly with the ggsql Rust library and allows you to set up readers and writers and execute queries against it. The package also offers knitr and shiny integration allowing the user to use ggsql in both frameworks.

This package provides an easy to use interface to the Google Pub/Sub REST API <https://cloud.google.com/pubsub/docs/reference/rest>.

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.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

Realize three approaches for Gene-Environment interaction analysis. All of them adopt Sparse Group Minimax Concave Penalty to identify important G variables and G-E interactions, and simultaneously respect the hierarchy between main G and G-E interaction effects. All the three approaches are available for Linear, Logistic, and Poisson regression. Also realize to mine and construct prior information for G variables and G-E interactions.

Wrappers for functions in the gRain package to emulate some RHugin functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both hard (boolean) or soft (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with RHugin', but this package should assist users without access to Hugin to use code written to use RHugin'.

This package provides a data visualization design that provides comparison between two (Double) data sources (usually on a par with each other) on one reformed heatmap, while inheriting ggplot2 features.

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