Showing 200 of total 778 results (show query)

sciviews

svHttp:'SciViews' - HTTP Server

A simple HTTP server allows to connect GUI clients to R.

Maintained by Philippe Grosjean. Last updated 3 years ago.

sciviewsserver

17.8 match 1 stars 3.70 score 7 scripts

melff

RKernel:Yet another R kernel for Jupyter

Provides a kernel for Jupyter.

Maintained by Martin Elff. Last updated 1 days ago.

jupyterjupyter-kerneljupyter-kernelsjupyter-notebook

12.4 match 38 stars 4.64 score

demsarjure

autohrf:Automated Generation of Data-Informed GLM Models in Task-Based fMRI Data Analysis

Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.

Maintained by Jure Demšar. Last updated 1 years ago.

10.8 match 2 stars 4.72 score 13 scripts

tiledb-inc

tiledbcloud:TileDB Cloud Platform R Client Package

The TileDB Cloud Platform API Client Package offers access to the TileDB Cloud service.

Maintained by John Kerl. Last updated 9 months ago.

7.2 match 1 stars 5.22 score 92 scripts

devopifex

communicate:Communicate Between 'Shiny' Client and Server

What the package does (one paragraph).

Maintained by John Coene. Last updated 10 months ago.

client-serverhttpjavascript

11.0 match 23 stars 3.06 score 5 scripts

r-lib

devtools:Tools to Make Developing R Packages Easier

Collection of package development tools.

Maintained by Jennifer Bryan. Last updated 6 months ago.

package-creation

1.7 match 2.4k stars 19.55 score 51k scripts 150 dependents

calderonsamuel

SSEparser:Parse Server-Sent Events

Functionality to parse server-sent events with a high-level interface that can be extended for custom applications.

Maintained by Samuel Calderon. Last updated 1 years ago.

http-requestsparserstream

7.5 match 1 stars 3.65 score 3 scripts 3 dependents

jimhester

knitrBootstrap:'knitr' Bootstrap Framework

A framework to create Bootstrap <http://getbootstrap.com/> HTML reports from 'knitr' 'rmarkdown'.

Maintained by Jim Hester. Last updated 1 years ago.

2.1 match 277 stars 9.04 score 123 scripts 1 dependents

colearendt

tidyjson:Tidy Complex 'JSON'

Turn complex 'JSON' data into tidy data frames.

Maintained by Cole Arendt. Last updated 2 years ago.

1.7 match 192 stars 10.64 score 522 scripts 7 dependents

usdaforestservice

gdalraster:Bindings to the 'Geospatial Data Abstraction Library' Raster API

Interface to the Raster API of the 'Geospatial Data Abstraction Library' ('GDAL', <https://gdal.org>). Bindings are implemented in an exposed C++ class encapsulating a 'GDALDataset' and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()', 'polygonize()', 'rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Includes raster 'calc()' to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster 'combine()' to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base 'graphics'. Bindings to a subset of the 'OGR' API are also included for managing vector data sources. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on 'GDAL' virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API.

Maintained by Chris Toney. Last updated 1 hours ago.

gdalgeospatialrastervectorcpp

1.5 match 41 stars 9.50 score 32 scripts 3 dependents

sahirbhatnagar

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

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

Maintained by Sahir Bhatnagar. Last updated 4 years ago.

1.7 match 60 stars 7.15 score 78 scripts

aw256

httpRequest:Basic HTTP Request

HTTP Request protocols. Implements the GET, POST and multipart POST request.

Maintained by Andreas Westfeld. Last updated 3 years ago.

10.8 match 1.00 score 7 scripts

neuwirthe

RColorBrewer:ColorBrewer Palettes

Provides color schemes for maps (and other graphics) designed by Cynthia Brewer as described at http://colorbrewer2.org.

Maintained by Erich Neuwirth. Last updated 3 years ago.

0.5 match 9 stars 14.38 score 106k scripts 8.2k dependents

klutometis

functional:Curry, Compose, and other higher-order functions

Curry, Compose, and other higher-order functions

Maintained by Peter Danenberg. Last updated 11 years ago.

1.6 match 1 stars 4.57 score 157 scripts 9 dependents

statnmap

cartomisc:Miscellaneous Tools for Spatial Data Manipulation and Analysis

Some useful tools for use with spatial data.

Maintained by Sebastien Rochette. Last updated 5 years ago.

1.6 match 10 stars 4.04 score 11 scripts

predictiveecology

NetLogoR:Build and Run Spatially Explicit Agent-Based Models

Build and run spatially explicit agent-based models using only the R platform. 'NetLogoR' follows the same framework as the 'NetLogo' software (Wilensky (1999) <http://ccl.northwestern.edu/netlogo/>) and is a translation in R of the structure and functions of 'NetLogo'. 'NetLogoR' provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed 'NetLogo' framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants <http://ccl.northwestern.edu/netlogo/models/Ants>) and Wolf-Sheep-Predation (<http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>), and a third, Butterfly, from Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all written using 'NetLogoR' are available. The 'NetLogo' code of the original version of these models is provided alongside. A programming guide inspired from the 'NetLogo' Programming Guide (<https://ccl.northwestern.edu/netlogo/docs/programming.html>) and a dictionary of 'NetLogo' primitives (<https://ccl.northwestern.edu/netlogo/docs/dictionary.html>) equivalences are also available. NOTE: To increment 'time', these functions can use a for loop or can be integrated with a discrete event simulator, such as 'SpaDES' (<https://cran.r-project.org/package=SpaDES>). The suggested package 'fastshp' can be installed with 'install.packages("fastshp", repos = ("<https://rforge.net>"), type = "source")'.

Maintained by Eliot J B McIntire. Last updated 4 months ago.

0.9 match 40 stars 6.96 score 19 scripts

htmlwidgets

sparkline:'jQuery' Sparkline 'htmlwidget'

Include interactive sparkline charts <http://omnipotent.net/jquery.sparkline> in all R contexts with the convenience of 'htmlwidgets'.

Maintained by Ramnath Vaidyanathan. Last updated 6 years ago.

0.5 match 245 stars 10.73 score 556 scripts 5 dependents

alexfoxfox

rChoiceDialogs:'rChoiceDialogs' Collection

Collection of portable choice dialog widgets.

Maintained by Alex Lisovich. Last updated 3 years ago.

openjdk

1.6 match 3.64 score 49 scripts 3 dependents

r-cas

Ryacas:R Interface to the 'Yacas' Computer Algebra System

Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).

Maintained by Mikkel Meyer Andersen. Last updated 2 years ago.

cpp

0.6 match 40 stars 10.15 score 167 scripts 14 dependents

rsginc

RSGHB:Functions for Hierarchical Bayesian Estimation: A Flexible Approach

Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.

Maintained by Jeff Dumont. Last updated 6 years ago.

0.9 match 26 stars 5.30 score 25 scripts 1 dependents

briencj

asremlPlus:Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences

Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.

Maintained by Chris Brien. Last updated 1 months ago.

asremlmixed-models

0.5 match 19 stars 9.37 score 200 scripts