Results for statistic-models

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

Chillout gem tracks your ActiveRecord models statistics.

Unidom (UNIfied Domain Object Model) is a series of domain model engines. The StAPaR (Statistical Approach of Pattern Recognition) domain model engine includes Sample and Matching models. Unidom (统一领域对象模型)是一系列的领域模型引擎。统计模式识别领域模型引擎包括采样和匹配的模型。

A performant and flexible counter solution that allows to make statistics on your models

Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business. CmdStanRb offers bindings in Ruby to employ Stan, targeting easing up the integration of Stan and Bayesian Inference into Ruby-powered environments.

Graphics for your rails models with MetricsGraphics.js and NVD3. It may show count(or average, or sum, or another sql agregate function) of models for each day with grouping, conditions. Uses Redis for store statistics.

Acts As Active is a tracker and activity generator for any ActiveRecord model, making it easy to generate statistics, streaks, and data visualization.

This uses distinct state machines for each behaviour, that have their own internal statistical model.

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.

In information retrieval, tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general.

Rails Console Pro enhances your Rails console with powerful debugging tools: - Beautiful colored formatting for ActiveRecord objects - Schema inspection with columns, indexes, associations, validations - SQL explain analysis with performance recommendations - Interactive association navigation - Model statistics (record counts, growth rates, table sizes) - Object diffing and comparison - Export to JSON, YAML, and HTML - Smart pagination for large collections

Ruby Scientist and Graphics is a practical data science toolkit for Ruby. It includes a lightweight built-in DataFrame for loading, cleaning, and transforming data; quick descriptive statistics and correlations; charting via Gruff (bar and line); and simple ML utilities (linear regression and k-means)—all behind a small, unified, pandas-inspired API. Key features: - Load data from CSV and JSON. - Clean and transform (remove/add columns, handle missing values, limit rows). - Describe datasets and compute correlations quickly. - Create bar and line charts with customization options. - Train/predict with linear regression; cluster with k-means. - Save/load project state (data + trained model) and run simple pipelines. - Optional backend adapters (e.g., Rover) while keeping the same API. Ideal for analysts and developers who want to explore data in Ruby without relying on Python or R. Note: plotting via Gruff uses rmagick, which requires ImageMagick installed on the system.

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