GitHub

StatsPAI: a Python-native Stata/R replacement for applied causal inference

StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a ...
GitHub

PyAF (Python Automatic Forecasting)

PyAF is an Open Source Python library for Automatic Forecasting built on top of popular data science python modules: NumPy, SciPy, Pandas and scikit-learn. PyAF works as an automated process for ...
C&EN

Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Coal-based electricity production in the United States has ...
Frontiers

Efficient use of binned data for imputing univariate time series data

Time series data are recorded in various sectors, resulting in a large amount of data. However, the continuity of these data is often interrupted, resulting in periods of missing data. Several ...
C&EN

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. No ...
Frontiers

Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield

Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four ...