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Showing 4 open source projects for "binary"
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blavaan
An R package for Bayesian structural equation modeling
...The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. The blavaan package depends on the lavaan package for model specification and for some computations. This means that, if you already know lavaan, then you should already be able to do many things in blavaan. In particular, many blavaan commands add the letter “b” to the start of the lavaan command. It is also sometimes possible to use a lavaan command on a blavaan object, though the results may not always be what you expect. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. ... -
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GDINA Package for Cognitively Diagnostic
Package for Cognitively Diagnostic Analyses
...Modelling independent, saturated, higher-order, loglinear smoothed, and structured joint attribute distribution. Accommodating multiple-group model analysis. Imposing monotonic constrained success probabilities. Accommodating binary and polytomous attributes. -
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sf (Simple Features)
Simple Features for R
sf is an R package that implements “simple features” (standardized vector spatial data) for R. It allows spatial vector data (points, lines, polygons etc.) to be represented as records in data frames (or tibbles) with geometry list columns, and performs spatial operations (geometry operations, coordinate reference system transformations, reading/writing spatial data, integration with spatial databases etc.). It interfaces to GDAL, GEOS, PROJ libraries for robust operations. Reading and... -
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