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An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1371/journal.pone.0254394

https://doi.org/10.1371/journal.pone.0254394

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods

Cited by: 0
Author(s): Kozo Hara, Yasuki Kobayashi, Jun Tomio, Yuki Ito, Thomas Svensson, Ryo Ikesu, Ung‐il Chung, Akiko Kishi Svensson
Published: almost 4 years ago

Software Mentions 10

cran: epiR
Tools for the Analysis of Epidemiological Data
Papers that mentioned: 199
Very Likely Science (93)
cran: glmnet
Lasso and Elastic-Net Regularized Generalized Linear Models
Papers that mentioned: 1,607
Very Likely Science (100)
cran: LiblineaR
Linear Predictive Models Based on the LIBLINEAR C/C++ Library
Papers that mentioned: 15
Very Likely Science (75)
cran: nnet
Feed-Forward Neural Networks and Multinomial Log-Linear Models
Papers that mentioned: 296
Very Likely Science (93)
cran: pROC
Display and Analyze ROC Curves
Papers that mentioned: 1,740
Very Likely Science (95)
cran: ranger
A Fast Implementation of Random Forests
Papers that mentioned: 91
Very Likely Science (100)
cran: xgboost
Extreme Gradient Boosting
Papers that mentioned: 207
Very Likely Science (100)
pypi: glmnet
Python wrapper for glmnet
Papers that mentioned: 1,607
Very Likely Science (100)
pypi: ranger
A Python package for the manipulation of Range objects
Papers that mentioned: 91
Very Likely Science (100)
pypi: xgboost
XGBoost Python Package
Papers that mentioned: 207
Very Likely Science (90)