Showing 144 of total 144 results (show query)

davidgohel

ggiraph:Make 'ggplot2' Graphics Interactive

Create interactive 'ggplot2' graphics using 'htmlwidgets'.

Maintained by David Gohel. Last updated 3 months ago.

libpngcpp

7.6 match 819 stars 14.39 score 4.1k scripts 34 dependents

dmurdoch

plotrix:Various Plotting Functions

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Maintained by Duncan Murdoch. Last updated 1 years ago.

5.4 match 5 stars 11.31 score 9.2k scripts 361 dependents

mbedward

packcircles:Circle Packing

Algorithms to find arrangements of non-overlapping circles.

Maintained by Michael Bedward. Last updated 4 months ago.

cpp

3.3 match 57 stars 10.06 score 422 scripts 6 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

2.3 match 15 stars 12.60 score 7.7k scripts 295 dependents

yulab-smu

ggfun:Miscellaneous Functions for 'ggplot2'

Useful functions and utilities for 'ggplot' object (e.g., geometric layers, themes, and utilities to edit the object).

Maintained by Guangchuang Yu. Last updated 2 days ago.

1.9 match 18 stars 10.53 score 58 scripts 152 dependents

karlines

shape:Functions for Plotting Graphical Shapes, Colors

Functions for plotting graphical shapes such as ellipses, circles, cylinders, arrows, ...

Maintained by Karline Soetaert. Last updated 1 years ago.

1.8 match 10.86 score 984 scripts 1.4k dependents

mhenderson

keedwell:Latin Squares in R

Completion and embedding of latin squares in R.

Maintained by Matthew Henderson. Last updated 10 months ago.

combinatoricslatin-squares

7.1 match 2.40 score 3 scripts

trevorld

pnpmisc:Utilities for Print-and-Play Board Games

Utilities for print-and-play board games.

Maintained by Trevor L. Davis. Last updated 15 days ago.

print-and-play

5.5 match 3.02 score 1 dependents

coolbutuseless

nara:Native Raster Image Tools

Tools for 'nativeRaster' images.

Maintained by Mike Cheng. Last updated 1 months ago.

gfxgraphicspkg

1.9 match 62 stars 6.32 score 42 scripts

david-barnett

microViz:Microbiome Data Analysis and Visualization

Microbiome data visualization and statistics tools built upon phyloseq.

Maintained by David Barnett. Last updated 3 months ago.

microbiomemicrobiome-analysismicrobiota

1.8 match 114 stars 6.22 score 480 scripts

yihui

fun:Use R for Fun

This is a collection of R games and other funny stuff, such as the classic Mine sweeper and sliding puzzles.

Maintained by Yihui Xie. Last updated 2 years ago.

1.7 match 47 stars 6.30 score 70 scripts

emiliotorres

xkcd:Plotting ggplot2 Graphics in an XKCD Style

Plotting ggplot2 graphs using the XKCD style.

Maintained by Emilio Torres-Manzanera. Last updated 7 years ago.

2.0 match 3 stars 4.34 score 244 scripts

ices-tools-prod

icesFO:Functions to support the creation of ICES Fisheries Overviews

Functions to support the creation of ICES Fisheries Overviews.

Maintained by Adriana Villamor. Last updated 9 months ago.

1.8 match 2 stars 3.41 score 260 scripts

sahirbhatnagar

manhattanly:Interactive Q-Q and Manhattan Plots Using 'plotly.js'

Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a point to show details or drag a rectangle to zoom. A manhattan plot is a popular graphical method for visualizing results from high-dimensional data analysis such as a (epi)genome wide association study (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter plot against their genomic position. Manhattan plots are used for visualizing potential regions of interest in the genome that are associated with a phenotype. Interactive manhattan plots allow the inspection of specific value (e.g. rs number or gene name) by hovering the mouse over a cell, as well as zooming into a region of the genome (e.g. a chromosome) by dragging a rectangle around the relevant area. This work is based on the 'qqman' package and the 'plotly.js' engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq' functions in the 'qqman' package, with the advantage of including extra annotation information and interactive web-based visualizations directly from R. Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) can be viewed and modified in a web browser.

Maintained by Sahir Bhatnagar. Last updated 4 years ago.

0.8 match 60 stars 7.15 score 78 scripts

jgraux

DepLogo:Dependency Logo

Plots dependency logos from a set of aligned input sequences.

Maintained by Jan Grau. Last updated 1 years ago.

2.0 match 1 stars 2.41 score 26 scripts

rwoldford

eikosograms:The Picture of Probability

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths, represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes theorem as a completely trivial consequence). They are markedly superior to Venn diagrams for this purpose, especially in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. As data analysis tools, eikosograms display categorical data in a manner similar to Mosaic plots, especially when only two variates are involved (the only case in which they are essentially identical, though eikosograms purposely disallow spacing between rectangles). Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate (beyond two) is introduced. Instead, only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model) than is a Mosaic plot. Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis. Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn appearing as the response. This makes it better suited than Mosaic plots to discrete graphical models based on conditional independence graphs (i.e. "Bayesian Networks" or "BayesNets"). The eikosogram and its superiority to Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.

Maintained by Wayne Oldford. Last updated 6 hours ago.

0.9 match 4 stars 4.92 score 14 scripts