Daru (Data Analysis in RUby) is a library for analysis, manipulation and visualization of data. Daru works seamlessly accross interpreters and leverages interpreter-specific optimizations whenever they are available. It is the default data storage gem for all the statsample gems (glm, timeseries, etc.) and can be used with many others like mixed_models, gnuplotrb, nyaplot and iruby.

Scientific gems for Ruby. This is the full installation with rigid version constraints.

A suite for basic and advanced statistics on Ruby. Tested on CRuby 1.9.3, 2.0.0 and 2.1.1. See `.travis.yml` for more information. Include: - Descriptive statistics: frequencies, median, mean, standard error, skew, kurtosis (and many others). - Correlations: Pearson's r, Spearman's rank correlation (rho), point biserial, tau a, tau b and gamma. Tetrachoric and Polychoric correlation provides by statsample-bivariate-extension gem. - Intra-class correlation - Anova: generic and vector-based One-way ANOVA and Two-way ANOVA, with contrasts for One-way ANOVA. - Tests: F, T, Levene, U-Mannwhitney. - Regression: Simple, Multiple (OLS), Probit and Logit - Factorial Analysis: Extraction (PCA and Principal Axis), Rotation (Varimax, Equimax, Quartimax) and Parallel Analysis and Velicer's MAP test, for estimation of number of factors. - Reliability analysis for simple scale and a DSL to easily analyze multiple scales using factor analysis and correlations, if you want it. - Dominance Analysis, with multivariate dependent and bootstrap (Azen & Budescu) - Sample calculation related formulas - Structural Equation Modeling (SEM), using R libraries +sem+ and +OpenMx+ - Creates reports on text, html and rtf, using ReportBuilder gem - Graphics: Histogram, Boxplot and Scatterplot.

Proper related posts plugin for Jekyll - uses document correlation matrix on TF-IDF (optionally with Latent Semantic Indexing). Each document is tokenized and stemmed, every word found is treated as keyword for analysis (except for some stop words). TF-IDF matrix for the whole site is calculated (including extra provided weights), then if given accuraccy is lower than 1.0, LSI algorithm is used to compute new simplified vector space. Document correlation matrix is created using dot product of the matrix and its transpose. For each of the post' related documents are inserted into priority queue (sorted by score from document correlation matrix), assuming the score is greater than minimal required score. Selected few bests related posts are retrieven from the queue. Liquid template for each post is rendered and <related-posts /> is replaced with the outcomes of algorithm.

DataFrame works with NMatrix, Statsample, and Nyaplot

Fit statistical (linear) models with fixed and mixed (random) effects in Ruby

Polychoric and Tetrachoric extensions for Statsample::Bivariate.

This directly implements a ruby version of the arcade learning environment interface.

Neural networks in Ruby and CUDA.

Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.

Runs Wavefront algorithm to find the shortest path from A to B

Methods for using maximum likelihood calculations to estimate parmeters of ratios of gaussian variates

A Ruby interface for reading and writing NetCDF files. NetCDF-NMatrix is a rewrite of the previous ruby-netcdf gem which uses NMatrix instead of NArray, and so is compatible with SciRuby.

Neuralnet for newbies

For using linear algebra fuctions provided by ATLAS

Extra functions for nmatrix

NMatrix extension for using fuctions provided by FFTW

For using linear algebra fuctions provided by LAPACK and BLAS

Arbitrary row/column selection for NMatrix

RandSVD is a class that performs truncated singular value decomposition using a randomized algorithm.