R-universe search: histograms

airpino

HistDAWass:Histogram-Valued Data Analysis

In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Maintained by Antonio Irpino. Last updated 1 years ago.

openblas cpp openmp

65.3 match 5 stars 4.75 score 75 scripts

thmild

histogram:Construction of Regular and Irregular Histograms with Different Options for Automatic Choice of Bins

Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).

Maintained by Thoralf Mildenberger. Last updated 6 years ago.

63.5 match 3.38 score 10 scripts 2 dependents

deepayan

lattice:Trellis Graphics for R

A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.

Maintained by Deepayan Sarkar. Last updated 11 months ago.

11.3 match 68 stars 17.33 score 27k scripts 13k dependents

tomkellygenetics

vioplot:Violin Plot

A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.

Maintained by S. Thomas Kelly. Last updated 19 days ago.

boxplot colours customisation dataviz formula plotting violin-plot violinplot vioplot

15.7 match 26 stars 12.32 score 2.0k scripts 8 dependents

ajrgodfrey

BrailleR:Improved Access for Blind Users

Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images.

Maintained by A. Jonathan R. Godfrey. Last updated 11 months ago.

17.2 match 123 stars 8.90 score 143 scripts

great-northern-diver

loon:Interactive Statistical Data Visualization

An extendable toolkit for interactive data visualization and exploration.

Maintained by R. Wayne Oldford. Last updated 2 years ago.

data-analysis data-science data-visualization exploratory-analysis exploratory-data-analysis high-dimensional-data interactive-graphics interactive-visualizations loon python statistical-analysis statistical-graphics statistics tcl-extension tk

16.4 match 48 stars 9.00 score 93 scripts 5 dependents

cran

agricolae:Statistical Procedures for Agricultural Research

Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.

Maintained by Felipe de Mendiburu. Last updated 1 years ago.

20.4 match 7 stars 7.01 score 15 dependents

bioc

structToolbox:Data processing & analysis tools for Metabolomics and other omics

An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.

Maintained by Gavin Rhys Lloyd. Last updated 24 days ago.

workflowstep metabolomics bioconductor-package dims lc-ms machine-learning multivariate-analysis statistics univariate

21.6 match 10 stars 6.26 score 12 scripts

uupharmacometrics

xpose4:Diagnostics for Nonlinear Mixed-Effect Models

A model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.

Maintained by Andrew C. Hooker. Last updated 1 years ago.

diagnostics nonmem pharmacometrics population-model xpose

17.7 match 35 stars 7.30 score 315 scripts

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 2 days ago.

fortran

7.3 match 210 stars 17.61 score 17k scripts 750 dependents

opengeos

whitebox:'WhiteboxTools' R Frontend

An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.

Maintained by Andrew Brown. Last updated 5 months ago.

geomorphometry geoprocessing geospatial gis hydrology remote-sensing rstudio

13.1 match 173 stars 9.65 score 203 scripts 2 dependents

mjskay

ggdist:Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.

Maintained by Matthew Kay. Last updated 4 months ago.

ggplot2 uncertainty uncertainty-visualization visualization cpp

8.3 match 856 stars 15.24 score 3.1k scripts 61 dependents

distancedevelopment

mrds:Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Maintained by Laura Marshall. Last updated 2 months ago.

15.3 match 4 stars 8.05 score 78 scripts 7 dependents

muschellij2

WhiteStripe:White Matter Normalization for Magnetic Resonance Images

Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008> introduced 'WhiteStripe', an intensity-based normalization of T1 and T2 images, where normal appearing white matter performs well, but requires segmentation. This method performs white matter mean and standard deviation estimates on data that has been rigidly-registered to the 'MNI' template and uses histogram-based methods.

Maintained by John Muschelli. Last updated 10 months ago.

19.5 match 9 stars 6.22 score 61 scripts

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.

9.6 match 5 stars 11.31 score 9.2k scripts 361 dependents

brry

berryFunctions:Function Collection Related to Plotting and Hydrology

Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.

Maintained by Berry Boessenkool. Last updated 1 months ago.

11.1 match 13 stars 9.43 score 350 scripts 16 dependents

dwoll

DVHmetrics:Analyze Dose-Volume Histograms and Check Constraints

Functionality for analyzing dose-volume histograms (DVH) in radiation oncology: Read DVH text files, calculate DVH metrics as well as generalized equivalent uniform dose (gEUD), biologically effective dose (BED), equivalent dose in 2 Gy fractions (EQD2), normal tissue complication probability (NTCP), and tumor control probability (TCP). Show DVH diagrams, check and visualize quality assurance constraints for the DVH. Includes web-based graphical user interface.

Maintained by Daniel Wollschlaeger. Last updated 15 days ago.

17.1 match 12 stars 6.03 score

bioc

flowPloidy:Analyze flow cytometer data to determine sample ploidy

Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards.

Maintained by Tyler Smith. Last updated 5 months ago.

flowcytometry gui regression visualization bioconductor evolution flow-cytometry polyploidy

16.1 match 5 stars 6.26 score 5 scripts

daattali

ggExtra:Add Marginal Histograms to 'ggplot2', and More 'ggplot2' Enhancements

Collection of functions and layers to enhance 'ggplot2'. The flagship function is 'ggMarginal()', which can be used to add marginal histograms/boxplots/density plots to 'ggplot2' scatterplots.

Maintained by Dean Attali. Last updated 9 months ago.

ggplot2 ggplot2-enhancements marginal-plots

7.4 match 387 stars 13.45 score 3.3k scripts 28 dependents

hms-dbmi

UpSetR:A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets

Creates visualizations of intersecting sets using a novel matrix design, along with visualizations of several common set, element and attribute related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.

Maintained by Jake Conway. Last updated 4 years ago.

gehlenborglab ggplot2 upset upsetr visualization

6.5 match 781 stars 15.33 score 4.8k scripts 42 dependents

hiweller

colordistance:Distance Metrics for Image Color Similarity

Loads and displays images, selectively masks specified background colors, bins pixels by color using either data-dependent or automatically generated color bins, quantitatively measures color similarity among images using one of several distance metrics for comparing pixel color clusters, and clusters images by object color similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written for use with organism coloration (reef fish color diversity, butterfly mimicry, etc), but easily applicable for any image set.

Maintained by Hannah Weller. Last updated 1 years ago.

11.6 match 37 stars 7.93 score 76 scripts 2 dependents

e-sensing

sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintained by Gilberto Camara. Last updated 30 days ago.

big-earth-data cbers earth-observation eo-datacubes geospatial image-time-series land-cover-classification landsat planetary-computer r-spatial remote-sensing rspatial satellite-image-time-series satellite-imagery sentinel-2 stac-api stac-catalog cpp

9.3 match 494 stars 9.50 score 384 scripts

indrajeetpatil

ggstatsplot:'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 18 days ago.

bayes-factors datascience dataviz effect-size ggplot-extension hypothesis-testing non-parametric-statistics regression-models statistical-analysis

5.9 match 2.1k stars 14.49 score 3.0k scripts 1 dependents

multimeric

HistDat:Summary Statistics for Histogram/Count Data

In some cases you will have data in a histogram format, where you have a vector of all possible observations, and a vector of how many times each observation appeared. You could expand this into a single 1D vector, but this may not be advisable if the counts are extremely large. 'HistDat' allows for the calculation of summary statistics without the need for expanding your data.

Maintained by Michael Milton. Last updated 4 years ago.

28.4 match 1 stars 3.00 score 7 scripts

dgerbing

lessR:Less Code, More Results

Each function replaces multiple standard R functions. For example, two function calls, Read() and CountAll(), generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, time series with aggregation and forecasting, color themes, and Trellis (facet) graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering of regression instructions for interpretative output, and interactive visualizations.

Maintained by David W. Gerbing. Last updated 1 months ago.

11.4 match 6 stars 7.47 score 394 scripts 3 dependents

cpsievert

histoslider:A Histogram Slider Input for 'Shiny'

A histogram slider input binding for use in 'Shiny'. Currently supports creating histograms from numeric, date, and 'date-time' vectors.

Maintained by Carson Sievert. Last updated 1 years ago.

histogram react shiny slider ui-components

19.0 match 27 stars 4.31 score 15 scripts

projectmosaic

mosaic:Project MOSAIC Statistics and Mathematics Teaching Utilities

Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

Maintained by Randall Pruim. Last updated 1 years ago.

6.1 match 93 stars 13.32 score 7.2k scripts 7 dependents

winvector

WVPlots:Common Plots for Analysis

Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'. Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, conditioned scatter plot with marginal densities, box and stem with matching theoretical distribution, and density with matching theoretical distribution.

Maintained by John Mount. Last updated 11 months ago.

9.9 match 85 stars 8.00 score 280 scripts

vandomed

dvmisc:Convenience Functions, Moving Window Statistics, and Graphics

Collection of functions for running and summarizing statistical simulation studies, creating visualizations (e.g. CART Shiny app, histograms with fitted probability mass/density functions), calculating moving-window statistics efficiently, and performing common computations.

Maintained by Dane R. Van Domelen. Last updated 4 years ago.

aic bmi histograms miscellaneous cpp

12.5 match 1 stars 6.18 score 125 scripts 8 dependents

jinkim3

kim:A Toolkit for Behavioral Scientists

A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.

Maintained by Jin Kim. Last updated 17 days ago.

16.2 match 7 stars 4.66 score 3 scripts

spatstat

spatstat.univar:One-Dimensional Probability Distribution Support for the 'spatstat' Family

Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.

Maintained by Adrian Baddeley. Last updated 10 days ago.

7.5 match 3 stars 9.93 score 1 scripts 239 dependents

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 3 months ago.

5.2 match 52 stars 13.94 score 29k scripts 317 dependents

bioc

ComplexHeatmap:Make Complex Heatmaps

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics.

Maintained by Zuguang Gu. Last updated 5 months ago.

software visualization sequencing clustering complex-heatmaps heatmap

4.3 match 1.3k stars 16.93 score 16k scripts 151 dependents

adafede

cascade:Contextualizing untargeted Annotation with Semi-quantitative Charged Aerosol Detection for pertinent characterization of natural Extracts

This package provides the infrastructure to perform Automated Composition Assessment of Natural Extracts.

Maintained by Adriano Rutz. Last updated 10 days ago.

metabolite annotation charged aerosol detector semi-quantitative natural products computational metabolomics specialized metabolome

12.5 match 2 stars 5.74 score 40 scripts 1 dependents

braverock

PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis

Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.

Maintained by Brian G. Peterson. Last updated 3 months ago.

4.5 match 222 stars 15.93 score 4.8k scripts 20 dependents

timelyportfolio

dataui:`Data-UI` Interactive Visualizations

Create sparklines and histograms with 'data-ui'.

Maintained by Kent Russell. Last updated 5 years ago.

data-ui htmlwidgets interactive plots react reactjs sparklines svg visualization vx

12.6 match 76 stars 5.61 score 107 scripts

hzhanghenry

RCircos:Circos 2D Track Plot

A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.

Maintained by Hongen Zhang. Last updated 3 years ago.

9.8 match 6 stars 7.21 score 298 scripts 3 dependents

kassambara

ggpubr:'ggplot2' Based Publication Ready Plots

The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.

Maintained by Alboukadel Kassambara. Last updated 2 years ago.

4.1 match 1.2k stars 16.68 score 65k scripts 409 dependents

cran

preseqR:Predicting Species Accumulation Curves

Originally as an R version of Preseq <doi:10.1038/nmeth.2375>, the package has extended its functionality to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018) <arXiv:1607.02804v3>.

Maintained by Chao Deng. Last updated 7 years ago.

15.0 match 1 stars 4.35 score 123 scripts 3 dependents

bioc

scPipe:Pipeline for single cell multi-omic data pre-processing

A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

Maintained by Shian Su. Last updated 3 months ago.

immunooncology software sequencing rnaseq geneexpression singlecell visualization sequencematching preprocessing qualitycontrol genomeannotation dataimport curl bzip2 xz-utils zlib cpp

7.0 match 68 stars 9.02 score 84 scripts

z267xu

ggmulti:High Dimensional Data Visualization

It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.

Maintained by Zehao Xu. Last updated 2 years ago.

10.3 match 6.11 score 36 scripts 4 dependents

gavinsimpson

analogue:Analogue and Weighted Averaging Methods for Palaeoecology

Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.

Maintained by Gavin L. Simpson. Last updated 6 months ago.

7.0 match 14 stars 8.96 score 185 scripts 4 dependents

oscarperpinan

rasterVis:Visualization Methods for Raster Data

Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.

Maintained by Oscar Perpinan Lamigueiro. Last updated 12 months ago.

5.3 match 85 stars 11.74 score 4.2k scripts 13 dependents

cran

MASS:Support Functions and Datasets for Venables and Ripley's MASS

Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

Maintained by Brian Ripley. Last updated 15 days ago.

5.8 match 19 stars 10.53 score 11k dependents

fishr-core-team

FSA:Simple Fisheries Stock Assessment Methods

A variety of simple fish stock assessment methods.

Maintained by Derek H. Ogle. Last updated 2 months ago.

fish fisheries fisheries-management fisheries-stock-assessment population-dynamics stock-assessment

5.5 match 68 stars 11.08 score 1.7k scripts 6 dependents

cran

RSDA:R to Symbolic Data Analysis

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Maintained by Oldemar Rodriguez. Last updated 1 years ago.

18.4 match 1 stars 3.26 score 3 dependents

joemsong

Ckmeans.1d.dp:Optimal, Fast, and Reproducible Univariate Clustering

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

Maintained by Joe Song. Last updated 2 years ago.

cpp

6.9 match 19 stars 8.62 score 339 scripts 19 dependents

rspatial

terra:Spatial Data Analysis

Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).

Maintained by Robert J. Hijmans. Last updated 4 hours ago.

geospatial raster spatial vector onetbb proj gdal geos cpp

3.3 match 559 stars 17.65 score 17k scripts 849 dependents

rspatial

raster:Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.

Maintained by Robert J. Hijmans. Last updated 2 months ago.

cpp

3.3 match 164 stars 17.05 score 58k scripts 555 dependents

feddelegrand7

ddplot:Create D3 Based SVG Graphics

Create 'D3' based 'SVG' ('Scalable Vector Graphics') graphics using a simple 'R' API. The package aims to simplify the creation of many 'SVG' plot types using a straightforward 'R' API. The package relies on the 'r2d3' 'R' package and the 'D3' 'JavaScript' library. See <https://rstudio.github.io/r2d3/> and <https://d3js.org/> respectively.

Maintained by Mohamed El Fodil Ihaddaden. Last updated 2 years ago.

d3 d3js dataviz javascript

9.6 match 44 stars 5.64 score 7 scripts

mark-andrews

psyntur:Helper Tools for Teaching Statistical Data Analysis

Provides functions and data-sets that are helpful for teaching statistics and data analysis. It was originally designed for use when teaching students in the Psychology Department at Nottingham Trent University.

Maintained by Mark Andrews. Last updated 4 months ago.

8.3 match 5 stars 6.41 score 50 scripts

mages

googleVis:R Interface to Google Charts

R interface to Google's chart tools, allowing users to create interactive charts based on data frames. Charts are displayed locally via the R HTTP help server. A modern browser with an Internet connection is required. The data remains local and is not uploaded to Google.

Maintained by Markus Gesmann. Last updated 10 months ago.

4.1 match 361 stars 12.98 score 2.4k scripts 11 dependents

suyusung

arm:Data Analysis Using Regression and Multilevel/Hierarchical Models

Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

Maintained by Yu-Sung Su. Last updated 4 months ago.

4.3 match 25 stars 12.38 score 3.3k scripts 89 dependents

bioc

EBImage:Image processing and analysis toolbox for R

EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data.

Maintained by Andrzej Oleś. Last updated 5 months ago.

visualization bioinformatics image-analysis image-processing cpp

4.0 match 71 stars 12.89 score 1.5k scripts 33 dependents

stan-dev

bayesplot:Plotting for Bayesian Models

Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.

Maintained by Jonah Gabry. Last updated 1 months ago.

bayesian ggplot2 mcmc pandoc stan statistical-graphics visualization

3.1 match 436 stars 16.69 score 6.5k scripts 98 dependents

xfim

ggmcmc:Tools for Analyzing MCMC Simulations from Bayesian Inference

Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).

Maintained by Xavier Fernández i Marín. Last updated 2 years ago.

bayesian-data-analysis ggplot2 graphical jags mcmc stan

4.3 match 112 stars 12.02 score 1.6k scripts 8 dependents

mlampros

OpenImageR:An Image Processing Toolkit

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

filtering gabor-feature-extraction gabor-filters hog-features image image-hashing processing rcpparmadillo recognition slic slico superpixels openblas cpp openmp

5.2 match 60 stars 9.86 score 358 scripts 8 dependents

cran

espadon:Easy Study of Patient DICOM Data in Oncology

Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.

Maintained by Cathy Fontbonne. Last updated 1 months ago.

cpp

17.9 match 2.85 score

tidyverse

ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Maintained by Thomas Lin Pedersen. Last updated 8 days ago.

data-visualisation visualisation

2.0 match 6.6k stars 25.10 score 645k scripts 7.5k dependents

bioc

MatrixQCvis:Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Maintained by Thomas Naake. Last updated 5 months ago.

visualization shinyapps gui qualitycontrol dimensionreduction metabolomics proteomics transcriptomics

10.4 match 4.74 score 4 scripts

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptox ord

4.8 match 27 stars 10.22 score 307 scripts 1 dependents

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 3 days ago.

3.5 match 845 stars 13.57 score 264 scripts 2 dependents

shotaochi

imagerExtra:Extra Image Processing Library Based on 'imager'

Provides advanced functions for image processing based on the package 'imager'.

Maintained by Shota Ochi. Last updated 2 years ago.

image-processing imager cpp

9.1 match 11 stars 5.22 score

rsquaredacademy

descriptr:Generate Descriptive Statistics

Generate descriptive statistics such as measures of location, dispersion, frequency tables, cross tables, group summaries and multiple one/two way tables.

Maintained by Aravind Hebbali. Last updated 4 months ago.

descriptive-statistics eda summary-statistics

6.5 match 34 stars 7.37 score 221 scripts

boxuancui

DataExplorer:Automate Data Exploration and Treatment

Automated data exploration process for analytic tasks and predictive modeling, so that users could focus on understanding data and extracting insights. The package scans and analyzes each variable, and visualizes them with typical graphical techniques. Common data processing methods are also available to treat and format data.

Maintained by Boxuan Cui. Last updated 1 years ago.

data-analysis data-exploration data-science eda visualization

4.3 match 519 stars 11.16 score 2.2k scripts

bioc

Voyager:From geospatial to spatial omics

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Maintained by Lambda Moses. Last updated 3 months ago.

geneexpression spatial transcriptomics visualization bioconductor eda esda exploratory-data-analysis omics spatial-statistics spatial-transcriptomics

5.3 match 87 stars 8.71 score 173 scripts

mlampros

KernelKnn:Kernel k Nearest Neighbors

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

cpp11 distance-metric kernel-methods knn rcpparmadillo openblas cpp openmp

5.1 match 17 stars 9.16 score 54 scripts 13 dependents

jverzani

UsingR:Data Sets, Etc. for the Text "Using R for Introductory Statistics", Second Edition

A collection of data sets to accompany the textbook "Using R for Introductory Statistics," second edition.

Maintained by John Verzani. Last updated 3 years ago.

9.4 match 1 stars 4.97 score 1.4k scripts

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

3.8 match 29 stars 12.34 score 6.6k scripts 931 dependents

bartonicek

plotscaper:Explore Your Data with Interactive Figures

A framework for creating interactive figures for data exploration. All plots are automatically linked and support several kinds of interactive features, including selection, zooming, panning, and parameter manipulation. The figures can be interacted with either manually, using a mouse and a keyboard, or by running code from inside an active R session.

Maintained by Adam Bartonicek. Last updated 22 days ago.

6.3 match 16 stars 7.36 score 23 scripts

egenn

rtemis:Machine Learning and Visualization

Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.

Maintained by E.D. Gennatas. Last updated 1 months ago.

data-science data-visualization machine-learning machine-learning-library visualization

6.5 match 145 stars 7.09 score 50 scripts 2 dependents

spatstat

spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family

Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

Maintained by Adrian Baddeley. Last updated 1 months ago.

cluster-detection confidence-intervals hypothesis-testing k-function roc-curves scan-statistics significance-testing simulation-envelopes spatial-analysis spatial-data-analysis spatial-sharpening spatial-smoothing spatial-statistics

4.5 match 1 stars 10.17 score 67 scripts 148 dependents

rsquaredacademy

olsrr:Tools for Building OLS Regression Models

Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.

Maintained by Aravind Hebbali. Last updated 4 months ago.

collinearity-diagnostics linear-models regression stepwise-regression

3.8 match 103 stars 12.19 score 1.4k scripts 4 dependents

spatstat

spatstat.geom:Geometrical Functionality of the 'spatstat' Family

Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)

Maintained by Adrian Baddeley. Last updated 4 hours ago.

classes-and-objects distance-calculation geometry geometry-processing images mensuration plotting point-patterns spatial-data spatial-data-analysis

3.8 match 7 stars 12.11 score 241 scripts 227 dependents

balexanderstats

ggsurvey:Simplifying `ggplot2` for Survey Data

Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data.

Maintained by Brittany Alexander. Last updated 3 years ago.

11.8 match 11 stars 3.78 score 11 scripts

vincentarelbundock

modelsummary:Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr' dynamic documents. Details can be found in Arel-Bundock (2022) <doi:10.18637/jss.v103.i01>.

Maintained by Vincent Arel-Bundock. Last updated 14 days ago.

3.3 match 926 stars 13.41 score 6.2k scripts 2 dependents

spsanderson

healthyR.ai:The Machine Learning and AI Modeling Companion to 'healthyR'

Hospital machine learning and ai data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include predicting length of stay, and readmits. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 2 months ago.

ai artificial-intelligence healthcareanalytics healthyr healthyverse machine-learning

6.0 match 16 stars 7.37 score 36 scripts 1 dependents

openintrostat

openintro:Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

Supplemental functions and data for 'OpenIntro' resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Maintained by Mine Çetinkaya-Rundel. Last updated 2 months ago.

data openintro

3.7 match 240 stars 11.39 score 6.0k scripts

jinkim3

exploratory:A Tool for Large-Scale Exploratory Analyses

Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages 'kim' and 'ezr'. Selected References: Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>. Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Kim (2023) <https://jinkim.science/docs/kim.pdf>. Kim (2021) <doi:10.5281/zenodo.4619237>. Kim (2020) <https://CRAN.R-project.org/package=ezr>. Simmons et al. (2011) <doi:10.1177/0956797611417632> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.

Maintained by Jin Kim. Last updated 1 years ago.

8.9 match 5 stars 4.75 score 45 scripts

richardgeveritt

ggsmc:Visualising Output from Sequential Monte Carlo and Ensemble-Based Methods

Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.

Maintained by Richard G Everitt. Last updated 2 months ago.

9.4 match 4.48 score 6 scripts

jbryer

likert:Analysis and Visualization Likert Items

An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.

Maintained by Jason Bryer. Last updated 3 years ago.

4.0 match 310 stars 10.22 score 480 scripts 2 dependents

tarnduong

ks:Kernel Smoothing

Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) <doi:10.1201/9780429485572>.

Maintained by Tarn Duong. Last updated 6 months ago.

3.9 match 6 stars 10.14 score 920 scripts 262 dependents

dreamrs

billboarder:Create Interactive Chart with the JavaScript 'Billboard' Library

Provides an 'htmlwidgets' interface to 'billboard.js', a re-usable easy interface JavaScript chart library, based on D3 v4+. Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts. All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps.

Maintained by Victor Perrier. Last updated 5 months ago.

data-visualization htmlwidgets

4.0 match 174 stars 9.74 score 96 scripts 4 dependents

ludovikcoba

rrecsys:Environment for Evaluating Recommender Systems

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Maintained by Ludovik Çoba. Last updated 3 years ago.

cpp

5.6 match 23 stars 6.84 score 25 scripts

uclahs-cds

BoutrosLab.plotting.general:Functions to Create Publication-Quality Plots

Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

Maintained by Paul Boutros. Last updated 5 months ago.

4.5 match 12 stars 8.36 score 414 scripts 6 dependents

iohprofiler

IOHanalyzer:Data Analysis Part of 'IOHprofiler'

The data analysis module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). This module provides statistical analysis methods for the benchmark data generated by optimization heuristics, which can be visualized through a web-based interface. The benchmark data is usually generated by the experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.

Maintained by Diederick Vermetten. Last updated 10 months ago.

cpp

7.4 match 24 stars 5.10 score 13 scripts

tsuchiya-lab

dsdp:Density Estimation with Semidefinite Programming

The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository <https://github.com/tsuchiya-lab/dsdp/>.

Maintained by Satoshi Kakihara. Last updated 2 years ago.

density-estimation semidefinite-programming fortran openblas

10.1 match 3.70 score 2 scripts

bioc

flowViz:Visualization for flow cytometry

Provides visualization tools for flow cytometry data.

Maintained by Mike Jiang. Last updated 5 months ago.

immunooncology infrastructure flowcytometry cellbasedassays visualization

5.0 match 7.44 score 231 scripts 12 dependents

adeverse

adegraphics:An S4 Lattice-Based Package for the Representation of Multivariate Data

Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.

Maintained by Aurélie Siberchicot. Last updated 8 months ago.

3.6 match 9 stars 10.37 score 386 scripts 6 dependents

adrianantico

AutoPlots:Creating Echarts Visualizations as Easy as Possible

Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Maintained by Adrian Antico. Last updated 10 months ago.

8.5 match 21 stars 4.32 score

ggobi

GGally:Extension to 'ggplot2'

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Maintained by Barret Schloerke. Last updated 10 months ago.

2.3 match 597 stars 16.15 score 17k scripts 154 dependents

matthias-da

robCompositions:Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Maintained by Matthias Templ. Last updated 25 days ago.

cpp

3.8 match 11 stars 9.19 score 226 scripts 2 dependents

adamlilith

statisfactory:Statistical and Geometrical Tools

A collection of statistical and geometrical tools including the aligned rank transform (ART; Higgins et al. 1990 <doi:10.4148/2475-7772.1443>; Peterson 2002 <doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011 <doi:10.1145/1978942.1978963>), 2-D histograms and histograms with overlapping bins, a function for making all possible formulae within a set of constraints, amongst others.

Maintained by Adam B. Smith. Last updated 5 months ago.

2d-histograms aligned-rank-transform sampling

10.3 match 3.38 score 16 scripts 1 dependents

r-quantities

units:Measurement Units for R Vectors

Support for measurement units in R vectors, matrices and arrays: automatic propagation, conversion, derivation and simplification of units; raising errors in case of unit incompatibility. Compatible with the POSIXct, Date and difftime classes. Uses the UNIDATA udunits library and unit database for unit compatibility checking and conversion. Documentation about 'units' is provided in the paper by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a vignette; see 'citation("units")' for details.

Maintained by Edzer Pebesma. Last updated 2 days ago.

udunits cpp

2.0 match 181 stars 17.28 score 3.3k scripts 1.2k dependents

immunomind

immunarch:Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.

Maintained by Vadim I. Nazarov. Last updated 12 months ago.

airr-analysis b-cell-receptor bcr bcr-repertoire bioinformatics ig ig-repertoire immune-repertoire immune-repertoire-analysis immune-repertoire-data immunoglobulin immunoinformatics immunology rep-seq repertoire-analysis single-cell single-cell-analysis t-cell-receptor tcr tcr-repertoire cpp

3.6 match 315 stars 9.49 score 203 scripts

paulponcet

statip:Statistical Functions for Probability Distributions and Regression

A collection of miscellaneous statistical functions for probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for the Bernoulli distribution, and 'distr2name()', 'name2distr()' for distribution names; probability density estimation: 'densityfun()'; most frequent value estimation: 'mfv()', 'mfv1()'; other statistical measures of location: 'cv()' (coefficient of variation), 'midhinge()', 'midrange()', 'trimean()'; construction of histograms: 'histo()', 'find_breaks()'; calculation of the Hellinger distance: 'hellinger()'; use of classical kernels: 'kernelfun()', 'kernel_properties()'; univariate piecewise-constant regression: 'picor()'.

Maintained by Paul Poncet. Last updated 5 years ago.

4.8 match 2 stars 7.17 score 73 scripts 52 dependents

bioc

R453Plus1Toolbox:A package for importing and analyzing data from Roche's Genome Sequencer System

The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided.

Maintained by Hans-Ulrich Klein. Last updated 5 months ago.

sequencing infrastructure dataimport datarepresentation visualization qualitycontrol reportwriting

9.8 match 3.48 score 10 scripts

dicook

nullabor:Tools for Graphical Inference

Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Maintained by Di Cook. Last updated 1 months ago.

3.2 match 57 stars 10.38 score 370 scripts 2 dependents

tweedell

motoRneuron:Analyzing Paired Neuron Discharge Times for Time-Domain Synchronization

The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> for a more thorough introduction.

Maintained by Andrew Tweedell. Last updated 6 years ago.

8.9 match 1 stars 3.74 score 11 scripts

erblast

easyalluvial:Generate Alluvial Plots with a Single Line of Code

Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> Their graphical grammar however is a bit more complex then that of a regular x/y plots. The 'ggalluvial' package made a great job of translating that grammar into 'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial plot, however there still remains a multi-layered complexity that makes it difficult to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple interface to this package that allows you to produce a decent alluvial plot from any dataframe in either long or wide format from a single line of code while also handling continuous data. It is meant to allow a quick visualisation of entire dataframes with a focus on different colouring options that can make alluvial plots a great tool for data exploration.

Maintained by Bjoern Koneswarakantha. Last updated 1 years ago.

5.4 match 110 stars 6.13 score 81 scripts 1 dependents

fredhutch

gimap:Calculate Genetic Interactions for Paired CRISPR Targets

Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.

Maintained by Candace Savonen. Last updated 2 days ago.

5.1 match 6.43 score 7 scripts

daya6489

SmartEDA:Summarize and Explore the Data

Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.

Maintained by Dayanand Ubrangala. Last updated 1 years ago.

analysis exploratory-data-analysis

4.5 match 42 stars 7.25 score 214 scripts

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

1.8 match 1.1k stars 18.63 score 16k scripts 239 dependents

cardiomoon

ggiraphExtra:Make Interactive 'ggplot2'. Extension to 'ggplot2' and 'ggiraph'

Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots. All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.

Maintained by Keon-Woong Moon. Last updated 4 years ago.

3.6 match 48 stars 8.93 score 402 scripts 3 dependents

salvatoremangiafico

rcompanion:Functions to Support Extension Education Program Evaluation

Functions and datasets to support Summary and Analysis of Extension Program Evaluation in R, and An R Companion for the Handbook of Biological Statistics. Vignettes are available at <https://rcompanion.org>.

Maintained by Salvatore Mangiafico. Last updated 30 days ago.

4.0 match 4 stars 8.01 score 2.4k scripts 5 dependents

tomasfryda

h2o:R Interface for the 'H2O' Scalable Machine Learning Platform

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Maintained by Tomas Fryda. Last updated 1 years ago.

3.9 match 3 stars 8.20 score 7.8k scripts 11 dependents

ropensci

skimr:Compact and Flexible Summaries of Data

A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.

Maintained by Elin Waring. Last updated 2 months ago.

peer-reviewed ropensci summary-statistics unconf unconf17

1.9 match 1.1k stars 16.80 score 18k scripts 14 dependents

r-forge

survey:Analysis of Complex Survey Samples

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs.

Maintained by "Thomas Lumley". Last updated 6 months ago.

cpp

2.3 match 1 stars 13.94 score 13k scripts 232 dependents

wilkelab

ggridges:Ridgeline Plots in 'ggplot2'

Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.

Maintained by Claus O. Wilke. Last updated 3 months ago.

1.9 match 418 stars 16.71 score 14k scripts 285 dependents

jcfaria

fdth:Frequency Distribution Tables, Histograms and Polygons

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

Maintained by José C. Faria. Last updated 1 years ago.

5.3 match 2 stars 5.87 score 107 scripts

jinseob2kim

jsmodule:'RStudio' Addins and 'Shiny' Modules for Medical Research

'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.

Maintained by Jinseob Kim. Last updated 23 hours ago.

medical rstudio-addins shiny shiny-modules statistics

3.6 match 21 stars 8.68 score 61 scripts

pachadotdev

cpp11armadillo:An 'Armadillo' Interface

Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.48550/arXiv.2408.11074>.

Maintained by Mauricio Vargas Sepulveda. Last updated 24 days ago.

armadillo cpp cpp11 hacktoberfest linear-algebra

3.4 match 9 stars 9.14 score 1 scripts 16 dependents

staffanbetner

rethinking:Statistical Rethinking book package

Utilities for fitting and comparing models

Maintained by Richard McElreath. Last updated 3 months ago.

5.7 match 5.42 score 4.4k scripts

rkabacoff

qacBase:Functions to Facilitate Exploratory Data Analysis

Functions for descriptive statistics, data management, and data visualization.

Maintained by Kabacoff Robert. Last updated 3 years ago.

eda statistics

6.0 match 1 stars 5.13 score 45 scripts

edgararuiz

dbplot:Simplifies Plotting Data Inside Databases

Leverages 'dplyr' to process the calculations of a plot inside a database. This package provides helper functions that abstract the work at three levels: outputs a 'ggplot', outputs the calculations, outputs the formula needed to calculate bins.

Maintained by Edgar Ruiz. Last updated 5 years ago.

5.3 match 9 stars 5.77 score 186 scripts

jamesotto852

ggdensity:Interpretable Bivariate Density Visualization with 'ggplot2'

The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface.

Maintained by James Otto. Last updated 1 years ago.

3.8 match 231 stars 8.11 score 185 scripts 2 dependents

bioc

flowCHIC:Analyze flow cytometric data using histogram information

A package to analyze flow cytometric data of complex microbial communities based on histogram images

Maintained by Author: Joachim Schumann. Last updated 5 months ago.

immunooncology cellbasedassays clustering flowcytometry software visualization

8.0 match 3.78 score 1 scripts

bioc

interacCircos:The Generation of Interactive Circos Plot

Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework.

Maintained by Zhe Cui. Last updated 5 months ago.

visualization

7.5 match 4.00 score 1 scripts

svkucheryavski

mdatools:Multivariate Data Analysis for Chemometrics

Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.

Maintained by Sergey Kucheryavskiy. Last updated 7 months ago.

4.0 match 35 stars 7.37 score 220 scripts 1 dependents

aalfons

r2spss:Format R Output to Look Like SPSS

Create plots and LaTeX tables that look like SPSS output for use in teaching materials. Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr. Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots).

Maintained by Andreas Alfons. Last updated 3 years ago.

7.1 match 3 stars 4.18 score 1 scripts

cran

flexclust:Flexible Cluster Algorithms

The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.

Maintained by Bettina Grün. Last updated 15 days ago.

5.1 match 3 stars 5.81 score 52 dependents

bioc

DAPAR:Tools for the Differential Analysis of Proteins Abundance with R

The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).

Maintained by Samuel Wieczorek. Last updated 5 months ago.

proteomics normalization preprocessing massspectrometry qualitycontrol go dataimport prostar1

5.5 match 2 stars 5.42 score 22 scripts 1 dependents

florianhartig

DHARMa:Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

Maintained by Florian Hartig. Last updated 11 days ago.

glmm regression regression-diagnostics residual

2.0 match 226 stars 14.74 score 2.8k scripts 10 dependents

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 8 days ago.

fisheries flr fisheries-modelling

3.3 match 16 stars 8.78 score 956 scripts 23 dependents

pwwang

plotthis:High-Level Plotting Built Upon 'ggplot2' and Other Plotting Packages

Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

Maintained by Panwen Wang. Last updated 18 days ago.

ggplot2 plotting single-cell

5.3 match 34 stars 5.46 score 2 scripts

aidangildea

duke:Creating a Color-Blind Friendly Duke Color Package

Generates visualizations with Duke’s official suite of colors in a color blind friendly way.

Maintained by Aidan Gildea. Last updated 1 years ago.

5.9 match 2 stars 4.88 score 15 scripts

markhogue

radsafer:Radiation Safety

Provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).

Maintained by Mark Hogue. Last updated 2 years ago.

7.2 match 2 stars 4.00 score 7 scripts

tvganesh

cricketr:Analyze Cricketers and Cricket Teams Based on ESPN Cricinfo Statsguru

Tools for analyzing performances of cricketers based on stats in ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers. The package can also be used to analyze team performances.

Maintained by Tinniam V Ganesh. Last updated 4 years ago.

5.2 match 62 stars 5.55 score 115 scripts

yesimiao

clttools:Central Limit Theorem Experiments (Theoretical and Simulation)

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

Maintained by Simiao Ye. Last updated 9 years ago.

20.5 match 1.40 score 25 scripts

hrbrmstr

vegalite:Tools to Encode Visualizations with the 'Grammar of Graphics'-Like 'Vega-Lite' 'Spec'

The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega' specifications. Functions exist which enable building a valid 'spec' from scratch or importing a previously created 'spec' file. Functions also exist to export 'spec' files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an 'htmlwidget'.

Maintained by Bob Rudis. Last updated 7 years ago.

data-visualization datavisualization vega-lite vega-lite-spec visualization widget

3.7 match 158 stars 7.60 score 84 scripts

bioc

qvalue:Q-value estimation for false discovery rate control

This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

Maintained by John D. Storey. Last updated 5 months ago.

multiplecomparisons

2.0 match 114 stars 14.06 score 3.0k scripts 139 dependents

great-northern-diver

zenplots:Zigzag Expanded Navigation Plots

Graphical tools for visualizing high-dimensional data along a path of alternating one- and two-dimensional plots. Note that this includes interactive graphics plots based on 'loon' in turn based on 'tcltk' (included as part of the standard R distribution). It also requires 'graph' from Bioconductor. For more detail on use and algorithms, see <doi:10.18637/jss.v095.i04>.

Maintained by Wayne Oldford. Last updated 1 years ago.

dimensional-data dimensional-plots graphical-systems pairs zigzag

5.3 match 3 stars 5.33 score 12 scripts 1 dependents

lucasvenez

precintcon:Precipitation Intensity, Concentration and Anomaly Analysis

It contains functions to analyze the precipitation intensity, concentration and anomaly.

Maintained by Lucas Venezian Povoa. Last updated 9 years ago.

6.5 match 10 stars 4.28 score 38 scripts

hrbrmstr

ggalt:Extra Coordinate Systems, 'Geoms', Statistical Transformations, Scales and Fonts for 'ggplot2'

A compendium of new geometries, coordinate systems, statistical transformations, scales and fonts for 'ggplot2', including splines, 1d and 2d densities, univariate average shifted histograms, a new map coordinate system based on the 'PROJ.4'-library along with geom_cartogram() that mimics the original functionality of geom_map(), formatters for "bytes", a stat_stepribbon() function, increased 'plotly' compatibility and the 'StateFace' open source font 'ProPublica'. Further new functionality includes lollipop charts, dumbbell charts, the ability to encircle points and coordinate-system-based text annotations.

Maintained by Bob Rudis. Last updated 2 years ago.

geom ggplot-extension ggplot2 ggplot2-geom ggplot2-scales

2.2 match 674 stars 12.59 score 2.3k scripts 7 dependents

tidymodels

infer:Tidy Statistical Inference

The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.

Maintained by Simon Couch. Last updated 6 months ago.

1.8 match 734 stars 15.69 score 3.5k scripts 17 dependents

eikeluedeling

decisionSupport:Quantitative Support of Decision Making under Uncertainty

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.

Maintained by Eike Luedeling. Last updated 11 months ago.

5.3 match 6 stars 5.17 score 123 scripts

jokergoo

circlize:Circular Visualization

Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data. The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.

Maintained by Zuguang Gu. Last updated 1 years ago.

1.8 match 983 stars 15.62 score 10k scripts 213 dependents

bioc

GenomicDistributions:GenomicDistributions: fast analysis of genomic intervals with Bioconductor

If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.

Maintained by Kristyna Kupkova. Last updated 5 months ago.

software genomeannotation genomeassembly datarepresentation sequencing coverage functionalgenomics visualization

3.7 match 26 stars 7.44 score 25 scripts

mmaechler

sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich

Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().

Maintained by Martin Maechler. Last updated 5 months ago.

2.5 match 11 stars 10.87 score 566 scripts 119 dependents

talgalili

gplots:Various R Programming Tools for Plotting Data

Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as ('bandplot', 'wapply'), - enhanced versions of standard plots ('barplot2', 'boxplot2', 'heatmap.2', 'smartlegend'), - manipulating colors ('col2hex', 'colorpanel', 'redgreen', 'greenred', 'bluered', 'redblue', 'rich.colors'), - calculating and plotting two-dimensional data summaries ('ci2d', 'hist2d'), - enhanced regression diagnostic plots ('lmplot2', 'residplot'), - formula-enabled interface to 'stats::lowess' function ('lowess'), - displaying textual data in plots ('textplot', 'sinkplot'), - plotting dots whose size reflects the relative magnitude of the elements ('balloonplot', 'bubbleplot'), - plotting "Venn" diagrams ('venn'), - displaying Open-Office style plots ('ooplot'), - plotting multiple data on same region, with separate axes ('overplot'), - plotting means and confidence intervals ('plotCI', 'plotmeans'), - spacing points in an x-y plot so they don't overlap ('space').

Maintained by Tal Galili. Last updated 5 months ago.

1.8 match 13 stars 15.11 score 13k scripts 482 dependents

adeverse

ade4:Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.

Maintained by Aurélie Siberchicot. Last updated 11 days ago.

openblas cpp

1.8 match 39 stars 14.96 score 2.2k scripts 256 dependents

sigbertklinke

exams.forge:Support for Compiling Examination Tasks using the 'exams' Package

The main aim is to further facilitate the creation of exercises based on the package 'exams' by Grün, B., and Zeileis, A. (2009) <doi:10.18637/jss.v029.i10>. Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.

Maintained by Sigbert Klinke. Last updated 8 months ago.

9.9 match 2.70 score 1 scripts

pmartr

pmartR:Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Maintained by Lisa Bramer. Last updated 2 days ago.

data-summarization lipids mass-spectrometry metabolites metabolomics-data peptides proteins rna-seq-analysis openblas cpp

3.5 match 40 stars 7.69 score 144 scripts

mw201608

NetWeaver:Graphic Presentation of Complex Genomic and Network Data Analysis

Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.

Maintained by Minghui Wang. Last updated 2 years ago.

5.6 match 4 stars 4.75 score 28 scripts

rich-iannone

DiagrammeR:Graph/Network Visualization

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.

Maintained by Richard Iannone. Last updated 2 months ago.

graph graph-functions network-graph property-graph visualization

1.8 match 1.7k stars 15.18 score 3.8k scripts 87 dependents

deepankardatta

blandr:Bland-Altman Method Comparison

Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 2015 as existing Bland-Altman analysis functions did not calculate confidence intervals. This package was created to rectify this, and create reproducible plots. This package is also available as a module for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).

Maintained by Deepankar Datta. Last updated 9 months ago.

bland-altman ggplot2 method-comparison statistics

3.7 match 22 stars 7.22 score 75 scripts

rkbauer

RchivalTag:Analyzing and Interactive Visualization of Archival Tagging Data

A set of functions to generate, access and analyze standard data products from archival tagging data.

Maintained by Robert K. Bauer. Last updated 2 months ago.

data-visuali depth depth-temperature-profiles dygraphs ggpot leaflet minipat pelagic plotly satellite sensor spatial star-oddi temperature time-series tracks wildlife-computers

7.3 match 1 stars 3.59 score 26 scripts

bfontez

DrBats:Data Representation: Bayesian Approach That's Sparse

Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.

Maintained by Benedicte Fontez. Last updated 3 years ago.

7.4 match 1 stars 3.51 score 13 scripts

sperfu

findGSEP:Estimate Genome Size of Polyploid Species Using k-Mer Frequencies

Provides tools to estimate the genome size of polyploid species using k-mer frequencies. This package includes functions to process k-mer frequency data and perform genome size estimation by fitting k-mer frequencies with a normal distribution model. It supports handling of complex polyploid genomes and offers various options for customizing the estimation process. The basic method 'findGSE' is detailed in Sun, Hequan, et al. (2018) <doi:10.1093/bioinformatics/btx637>.

Maintained by Laiyi Fu. Last updated 8 months ago.

5.3 match 3 stars 4.88 score 1 scripts

mjskay

tidybayes:Tidy Data and 'Geoms' for Bayesian Models

Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.

Maintained by Matthew Kay. Last updated 6 months ago.

bayesian-data-analysis brms ggplot2 jags stan tidy-data visualization

1.8 match 732 stars 14.88 score 7.3k scripts 19 dependents

glsnow

TeachingDemos:Demonstrations for Teaching and Learning

Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.

Maintained by Greg Snow. Last updated 1 years ago.

3.6 match 7.18 score 760 scripts 13 dependents

statistikat

VIM:Visualization and Imputation of Missing Values

New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.

Maintained by Matthias Templ. Last updated 7 months ago.

hotdeck imputation-methods model-predictions visualization cpp

1.8 match 85 stars 14.44 score 2.6k scripts 19 dependents

asgr

magicaxis:Pretty Scientific Plotting with Minor-Tick and Log Minor-Tick Support

Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.

Maintained by Aaron Robotham. Last updated 5 months ago.

3.8 match 9 stars 6.84 score 184 scripts 7 dependents

davidgohel

ggiraph:Make 'ggplot2' Graphics Interactive

Create interactive 'ggplot2' graphics using 'htmlwidgets'.

Maintained by David Gohel. Last updated 3 months ago.

libpng cpp

1.8 match 819 stars 14.39 score 4.1k scripts 34 dependents

braverock

PortfolioAnalytics:Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Maintained by Brian G. Peterson. Last updated 3 months ago.

2.3 match 81 stars 11.49 score 626 scripts 2 dependents

johncoene

echarts4r:Create Interactive Graphs with 'Echarts JavaScript' Version 5

Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes 36 chart types, themes, 'Shiny' proxies and animations.

Maintained by David Munoz Tord. Last updated 1 days ago.

echarts hacktoberfest htmlwidget htmlwidgets visualization

2.3 match 603 stars 11.45 score 1.3k scripts 11 dependents

gavinsimpson

gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

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.

Maintained by Gavin L. Simpson. Last updated 4 days ago.

distributional-regression gam gamm generalized-additive-mixed-models generalized-additive-models ggplot2 glm lm mgcv penalized-spline random-effects smoothing splines

2.0 match 216 stars 12.68 score 1.6k scripts 1 dependents

silentspringinstitute

RNHANES:Facilitates Analysis of CDC NHANES Data

Tools for downloading and analyzing CDC NHANES data, with a focus on analytical laboratory data.

Maintained by Herb Susmann. Last updated 9 hours ago.

nhanes publichealth

3.3 match 77 stars 7.58 score 83 scripts

hta-pharma

maicplus:Matching Adjusted Indirect Comparison

Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.

Maintained by Isaac Gravestock. Last updated 22 days ago.

3.4 match 5 stars 7.37 score 16 scripts

bioc

MetMashR:Metabolite Mashing with R

A package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included.

Maintained by Gavin Rhys Lloyd. Last updated 5 months ago.

workflowstep metabolomics kegg

4.3 match 2 stars 5.81 score 5 scripts

cheuerde

plotcli:Command Line Interface Plotting

The 'plotcli' package provides terminal-based plotting in R. It supports colored scatter plots, line plots, bar plots, and box plots. The package allows users to customize plot appearance, add titles, labels, ticks, and legends, and output the plot as a text-based visualization.

Maintained by Claas Heuer. Last updated 11 months ago.

4.1 match 10 stars 5.86 score 12 scripts

weecology

LDATS:Latent Dirichlet Allocation Coupled with Time Series Analyses

Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.

Maintained by Juniper L. Simonis. Last updated 5 years ago.

changepoint lda parallel-tempering portal softmax

3.4 match 25 stars 6.93 score 45 scripts

microsoft

wpa:Tools for Analysing and Visualising Viva Insights Data

Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in 'wpa': (i) Standard functions create a 'ggplot' visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to 'tidyverse' principles and works well with the pipe syntax. 'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity.

Maintained by Martin Chan. Last updated 4 months ago.

workplace-analytics

3.5 match 30 stars 6.69 score 39 scripts 1 dependents

lsteinmann

datplot:Preparation of Object Dating Ranges for Density Plots (Aoristic Analysis)

Converting date ranges into dating 'steps' eases the visualization of changes in e.g. pottery consumption, style and other variables over time. This package provides tools to process and prepare data for visualization and employs the concept of aoristic analysis.

Maintained by Lisa Steinmann. Last updated 1 years ago.

aoristic-analyses archaeology chronology visualization

4.2 match 10 stars 5.58 score 38 scripts

ropensci

visdat:Preliminary Visualisation of Data

Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.

Maintained by Nicholas Tierney. Last updated 8 months ago.

exploratory-data-analysis missingness peer-reviewed ropensci visualisation

1.8 match 452 stars 13.31 score 2.1k scripts 11 dependents

projectmosaic

ggformula:Formula Interface to the Grammar of Graphics

Provides a formula interface to 'ggplot2' graphics.

Maintained by Randall Pruim. Last updated 12 months ago.

2.0 match 38 stars 11.60 score 1.7k scripts 25 dependents

mlr-org

mlr3pipelines:Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

Maintained by Martin Binder. Last updated 7 days ago.

bagging data-science dataflow-programming ensemble-learning machine-learning mlr3 pipelines preprocessing stacking

1.9 match 141 stars 12.36 score 448 scripts 7 dependents

mahito-sugiyama

graphkernels:Graph Kernels

A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).

Maintained by Mahito Sugiyama. Last updated 3 years ago.

cpp

13.8 match 1.68 score 48 scripts

carstenlange

TeachHist:A Collection of Amended Histograms Designed for Teaching Statistics

Statistics students often have problems understanding the relation between a random variable's true scale and its z-values. To allow instructors to better better visualize histograms for these students, the package provides histograms with two horizontal axis containing z-values and the true scale of the variable. The function TeachHistDens() provides a density histogram with two axis. TeachHistCounts() and TeachHistRelFreq() are variations for count and relative frequency histograms, respectively. TeachConfInterv() and TeachHypTest() help instructors to visualize confidence levels and the results of hypothesis tests.

Maintained by Carsten Lange. Last updated 1 years ago.

14.5 match 1 stars 1.59 score 39 scripts

sonsoleslp

tna:Transition Network Analysis (TNA)

Provides tools for performing Transition Network Analysis (TNA) to study relational dynamics, including functions for building and plotting TNA models, calculating centrality measures, and identifying dominant events and patterns. TNA statistical techniques (e.g., bootstrapping and permutation tests) ensure the reliability of observed insights and confirm that identified dynamics are meaningful. See (Saqr et al., 2025) <doi:10.1145/3706468.3706513> for more details on TNA.

Maintained by Sonsoles López-Pernas. Last updated 23 hours ago.

educational-data-mining learning-analytics markov-model temporal-analysis

3.5 match 4 stars 6.48 score 5 scripts

r-forge

fAssets:Rmetrics - Analysing and Modelling Financial Assets

A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.

Maintained by Stefan Theussl. Last updated 3 months ago.

3.9 match 1 stars 5.93 score 26 scripts 3 dependents

jtlandis

ggside:Side Grammar Graphics

The grammar of graphics as shown in 'ggplot2' has provided an expressive API for users to build plots. 'ggside' extends 'ggplot2' by allowing users to add graphical information about one of the main panel's axis using a familiar 'ggplot2' style API with tidy data. This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution.

Maintained by Justin Landis. Last updated 10 months ago.

2.3 match 346 stars 10.18 score 349 scripts 6 dependents

stuart-lab

Signac:Analysis of Single-Cell Chromatin Data

A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.

Maintained by Tim Stuart. Last updated 7 months ago.

atac bioinformatics single-cell zlib cpp

1.9 match 349 stars 12.19 score 3.7k scripts 1 dependents

cran

ncappc:NCA Calculations and Population Model Diagnosis

A flexible tool that can perform (i) traditional non-compartmental analysis (NCA) and (ii) Simulation-based posterior predictive checks for population pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.

Maintained by Andrew C. Hooker. Last updated 7 years ago.

8.5 match 2.70 score

cran

mc2d:Tools for Two-Dimensional Monte-Carlo Simulations

A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).

Maintained by Regis Pouillot. Last updated 9 months ago.

3.6 match 1 stars 6.28 score 16 dependents

okgreece

DescriptiveStats.OBeu:Descriptive Statistics 'OpenBudgets.eu'

Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.

Maintained by Kleanthis Koupidis. Last updated 4 years ago.

boxplot correlation descriptive-statistics estimate frequencies obeu open-budgets openbudgets

4.2 match 1 stars 5.40 score 28 scripts 1 dependents

bioc

scDiagnostics:Cell type annotation diagnostics

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

Maintained by Anthony Christidis. Last updated 5 months ago.

annotation classification clustering geneexpression rnaseq singlecell software transcriptomics

2.9 match 8 stars 7.77 score 46 scripts

privefl

bigutilsr:Utility Functions for Large-scale Data

Utility functions for large-scale data. For now, package 'bigutilsr' mainly includes functions for outlier detection and unbiased PCA projection.

Maintained by Florian Privé. Last updated 5 months ago.

cpp

3.9 match 10 stars 5.80 score 39 scripts 5 dependents

bioc

qckitfastq:FASTQ Quality Control

Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers.

Maintained by August Guang. Last updated 5 months ago.

software qualitycontrol sequencing zlib cpp

5.1 match 4.38 score 24 scripts

priism-center

plotBart:Diagnostic and Plotting Functions to Supplement 'bartCause'

Functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the 'bartCause' package.

Maintained by Joseph Marlo. Last updated 10 months ago.

bart causal-inference

5.1 match 2 stars 4.30 score 20 scripts

marcjwilliams1

neutralitytestr:Test for a Neutral Evolutionary Model in Cancer Sequencing Data

Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.

Maintained by Marc Williams. Last updated 4 years ago.

4.3 match 10 stars 5.10 score 25 scripts

munterfi

eRTG3D:Empirically Informed Random Trajectory Generation in 3-D

Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>. Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.

Maintained by Merlin Unterfinger. Last updated 3 years ago.

3d birds conditional-empirical-random-walk gliding-and-soaring machine-learning movement-ecology random-trajectory-generator random-walk simulation trajectory-generation

3.8 match 6 stars 5.71 score 19 scripts

r-forge

deSolve:Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')

Functions that solve initial value problems of a system of first-order ordinary differential equations ('ODE'), of partial differential equations ('PDE'), of differential algebraic equations ('DAE'), and of delay differential equations. The functions provide an interface to the FORTRAN functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK' collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk' and a C-implementation of solvers of the 'Runge-Kutta' family with fixed or variable time steps. The package contains routines designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D partial differential equations ('PDE') that have been converted to 'ODEs' by numerical differencing.

Maintained by Thomas Petzoldt. Last updated 1 years ago.

fortran openblas

1.8 match 12.33 score 8.0k scripts 427 dependents

jkropko

coxed:Duration-Based Quantities of Interest for the Cox Proportional Hazards Model

Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.

Maintained by "Kropko, Jonathan". Last updated 4 years ago.

3.6 match 25 stars 6.00 score 132 scripts 1 dependents

cran

aplpack:Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots', Slider Functions and Others

Some functions for drawing some special plots: The function 'bagplot' plots a bagplot, 'faces' plots chernoff faces, 'iconplot' plots a representation of a frequency table or a data matrix, 'plothulls' plots hulls of a bivariate data set, 'plotsummary' plots a graphical summary of a data set, 'puticon' adds icons to a plot, 'skyline.hist' combines several histograms of a one dimensional data set in one plot, 'slider' functions supports some interactive graphics, 'spin3R' helps an inspection of a 3-dim point cloud, 'stem.leaf' plots a stem and leaf plot, 'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.

Maintained by Hans Peter Wolf. Last updated 3 years ago.

4.1 match 3 stars 5.25 score 5 dependents

microsoft

vivainsights:Analyze and Visualize Data from 'Microsoft Viva Insights'

Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.

Maintained by Martin Chan. Last updated 22 days ago.

3.5 match 11 stars 6.12 score 68 scripts

bioc

EMDomics:Earth Mover's Distance for Differential Analysis of Genomics Data

The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests.

Maintained by Sadhika Malladi. Last updated 5 months ago.

software differentialexpression geneexpression microarray

5.1 match 4.23 score 17 scripts

epiforecasts

scoringutils:Utilities for Scoring and Assessing Predictions

Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper, <doi:10.48550/arXiv.2205.07090>.

Maintained by Nikos Bosse. Last updated 12 days ago.

forecast-evaluation forecasting

1.9 match 52 stars 11.37 score 326 scripts 7 dependents

bayesball

LearnBayes:Learning Bayesian Inference

Contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Maintained by Jim Albert. Last updated 7 years ago.

1.9 match 38 stars 11.34 score 690 scripts 31 dependents

kiangkiangkiang

ggESDA:Exploratory Symbolic Data Analysis with 'ggplot2'

Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.

Maintained by Bo-Syue Jiang. Last updated 2 years ago.

5.3 match 21 stars 4.02 score 9 scripts

bioc

barcodetrackR:Functions for Analyzing Cellular Barcoding Data

barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.

Maintained by Diego Alexander Espinoza. Last updated 5 months ago.

software visualization sequencing

4.5 match 5 stars 4.70 score 6 scripts

jbengler

tidyplots:Tidy Plots for Scientific Papers

The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.

Maintained by Jan Broder Engler. Last updated 2 days ago.

2.3 match 482 stars 9.40 score 85 scripts

ebmtnprof

rties:Modeling Interpersonal Dynamics

The name of this package grew out of our research on temporal interpersonal emotion systems (TIES), hence 'rties'. It provides tools for using a set of models to investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly from both partners, extract the parameter estimates for each dyad, and then use those parameter estimates as input to a latent profile analysis to extract groups of dyads with qualitatively distinct dynamics. Finally, the profile memberships can be used to either predict, or be predicted by, another variable of interest. Currently, 2 models are supported: 1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided in vignettes. Theoretical background can be found in Butler (2011) <doi:10.1177/1088868311411164> and Butler & Barnard (2019) <doi:10.1097/PSY.0000000000000703>.

Maintained by Emily Butler. Last updated 3 years ago.

5.3 match 11 stars 3.94 score 16 scripts

bioc

demuxmix:Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models

A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

Maintained by Hans-Ulrich Klein. Last updated 5 months ago.

singlecell sequencing preprocessing classification regression

3.6 match 5 stars 5.76 score 19 scripts 1 dependents

choonghyunryu

dlookr:Tools for Data Diagnosis, Exploration, Transformation

A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.

Maintained by Choonghyun Ryu. Last updated 9 months ago.

1.9 match 212 stars 11.05 score 748 scripts 2 dependents

mmedl94

lionfish:Interactive 'tourr' Using 'python'

Extends the functionality of the 'tourr' package by an interactive graphical user interface. The interactivity allows users to effortlessly refine their 'tourr' results by manual intervention, which allows for integration of expert knowledge and aids the interpretation of results. For more information on 'tourr' see Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or <https://github.com/ggobi/tourr>.

Maintained by Matthias Medl. Last updated 3 days ago.

data-sience data-visualization dimensionality-reduction exploratory-data-analysis interactive interactive-visualizations tourr

3.5 match 1 stars 5.96 score

lionel-

ggstance:Horizontal 'ggplot2' Components

A 'ggplot2' extension that provides flipped components: horizontal versions of 'Stats' and 'Geoms', and vertical versions of 'Positions'. This package is now superseded by 'ggplot2' itself which now has full native support for horizontal layouts. It remains available for backward compatibility.

Maintained by Lionel Henry. Last updated 10 months ago.

1.9 match 201 stars 10.96 score 1.0k scripts 5 dependents

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 6 days ago.

analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference

2.3 match 5 stars 9.09 score 6 scripts 46 dependents

aviralvijay-gslab

nonet:Weighted Average Ensemble without Training Labels

It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.

Maintained by Aviral Vijay. Last updated 6 years ago.

6.0 match 1 stars 3.41 score 17 scripts

jamiemkass

ENMeval:Automated Tuning and Evaluations of Ecological Niche Models

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.

Maintained by Jamie M. Kass. Last updated 2 months ago.

1.8 match 49 stars 11.25 score 332 scripts 2 dependents