|
r-heatmaply
|
public |
Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional data, in which a table of numbers are encoded as a grid of colored cells. The rows and columns of the matrix are ordered to highlight patterns and are often accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing observations, correlations, missing values patterns, and more. Interactive 'heatmaps' allow the inspection of specific value by hovering the mouse over a cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle around the relevant area. This work is based on the 'ggplot2' and 'plotly.js' engine. It produces similar 'heatmaps' to 'heatmap.2' with the advantage of speed ('plotly.js' is able to handle larger size matrix), the ability to zoom from the 'dendrogram' panes, and the placing of factor variables in the sides of the 'heatmap'.
|
2025-04-22 |
|
r-heatmap3
|
public |
An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features.
|
2025-04-22 |
|
r-healthyr.ts
|
public |
Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
|
2025-04-22 |
|
r-hdm
|
public |
Implementation of selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly valid confidence intervals for various low-dimensional causal/ structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors and for instrumental variable (IV) and treatment effect estimation in a high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely on a theoretically grounded, data-driven choice of the penalty. Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
|
2025-04-22 |
|
r-hdclassif
|
public |
Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
|
2025-04-22 |
|
r-hdi
|
public |
Implementation of multiple approaches to perform inference in high-dimensional models.
|
2025-04-22 |
|
r-harrypotter
|
public |
Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise.
|
2025-04-22 |
|
r-haplor
|
public |
A set of utilities for querying 'HaploReg' <https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php>, 'RegulomeDB' <https://www.regulomedb.org/regulome-search/> web-based tools. The package connects to 'HaploReg', 'RegulomeDB' searches and downloads results, without opening web pages, directly from R environment. Results are stored in a data frame that can be directly used in various kinds of downstream analyses.
|
2025-04-22 |
|
r-hablar
|
public |
Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.
|
2025-04-22 |
|
r-gwqs
|
public |
Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>) and a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
|
2025-04-22 |
|
r-gtrendsr
|
public |
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.
|
2025-04-22 |
|
r-gtsummary
|
public |
Creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.
|
2025-04-22 |
|
r-gtfsio
|
public |
Tools for the development of packages related to General Transit Feed Specification (GTFS) files. Establishes a standard for representing GTFS feeds using R data types. Provides fast and flexible functions to read and write GTFS feeds while sticking to this standard. Defines a basic 'gtfs' class which is meant to be extended by packages that depend on it. And offers utility functions that support checking the structure of GTFS objects.
|
2025-04-22 |
|
r-gtextras
|
public |
Provides additional functions for creating beautiful tables with 'gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.
|
2025-04-22 |
|
r-gson
|
public |
Proposes a new file format ('gson') for storing gene set and related information, and provides read, write and other utilities to process this file format.
|
2025-04-22 |
|
r-gt
|
public |
Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which 'gt' handles things for you means that you don't often have to worry about the fine details.
|
2025-04-22 |
|
r-gsodr
|
public |
Provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the 'GSOD' data from 'NCEI', please see the 'GSOD' 'readme.txt' file available from, <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
|
2025-04-22 |
|
r-groupdata2
|
public |
Methods for dividing data into groups. Create balanced partitions and cross-validation folds. Perform time series windowing and general grouping and splitting of data. Balance existing groups with up- and downsampling or collapse them to fewer groups.
|
2025-04-22 |
|
r-groundhog
|
public |
Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.
|
2025-04-22 |
|
r-gridpattern
|
public |
Provides 'grid' grobs that fill in a user-defined area with various patterns. Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.
|
2025-04-22 |
|
r-grates
|
public |
Provides a coherent interface and implementation for creating grouped date classes. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
|
2025-04-22 |
|
r-gratia
|
public |
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
|
2025-04-22 |
|
r-grafify
|
public |
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify-vignettes.netlify.app/>. Citation: <doi:10.5281/zenodo.5136508>.
|
2025-04-22 |
|
r-gprofiler2
|
public |
A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler/>). The main tools are: (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists; (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces; (3) 'g:Orth' - orthology search across species; (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects. This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.
|
2025-04-22 |
|
r-gptstudio
|
public |
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>.
|
2025-04-22 |
