bigKRLS 2016 election analysis and simulations
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Replication materials for 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS' that focus on the 2016 US Presidential election and the county-level data used to assess 'communities in crisis' hypothesis outlined in the paper. Code is also provided for a number of simulations of Type 1 and Type 2 error rate, predictiveness, etc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["KRLS"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Replication materials for 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS' that focus on the 2016 US Presidential election and the county-level data used to assess 'communities in crisis' hypothesis outlined in the paper. Code is also provided for a number of simulations of Type 1 and Type 2 error rate, predictiveness, etc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Replication materials for 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS' that focus on the 2016 US Presidential election and the county-level data used to assess 'communities in crisis' hypothesis outlined in the paper. Code is also provided for a number of simulations of Type 1 and Type 2 error rate, predictiveness, etc
KRLS