Showing 5 of total 5 results (show query)
asgr
imager:Image Processing Library Based on 'CImg'
Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.
Maintained by Aaron Robotham. Last updated 6 days ago.
17 stars 13.53 score 2.4k scripts 44 dependents
andyliaw-mrk
randomForest:Breiman and Cutlers Random Forests for Classification and Regression
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
Maintained by Andy Liaw. Last updated 6 months ago.
46 stars 12.23 score 35k scripts 282 dependents
odelmarcelle
sentopics:Tools for Joint Sentiment and Topic Analysis of Textual Data
A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment.
Maintained by Olivier Delmarcelle. Last updated 3 months ago.
8 stars 5.38 score 5 scripts
softwaredeng
RRF:Regularized Random Forest
Feature Selection with Regularized Random Forest. This package is based on the 'randomForest' package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest. Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng, Regularized random forest for regression by Xin Guan. Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.
Maintained by Houtao Deng. Last updated 5 months ago.
3.81 score 118 scripts 3 dependents
cran
FamilyRank:Algorithm for Ranking Predictors Using Graphical Domain Knowledge
Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.
Maintained by Michelle Saul. Last updated 4 years ago.
1.00 score