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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference |
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Identification Number: |
doi:10.7910/DVN/DI19IB |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2021-03-23 |
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Version: |
1 |
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Bibliographic Citation: |
Goplerud, Max, 2021, "Replication Data for: Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference", https://doi.org/10.7910/DVN/DI19IB, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference |
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Identification Number: |
doi:10.7910/DVN/DI19IB |
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Authoring Entity: |
Goplerud, Max (University of Pittsburgh) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Goplerud, Max |
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Depositor: |
Goplerud, Max |
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Date of Deposit: |
2021-03-06 |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
Replication data for the results in Goplerud (2021) "Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference". |
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Notes: |
The R package to estimate the variational models can be installed from https://github.com/mgoplerud/vglmer The original data from Ghitza and Gelman (2013) should be downloaded from their dataverse: https://doi.org/10.7910/DVN/PZAOO6 The README file provides a description of the files and data structure. An ungated link to the paper can be found here: https://arxiv.org/abs/2007.12300 |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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Label: |
README.md |
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Text: |
Readme; also inside zip file |
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Notes: |
text/markdown |
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Label: |
replication_data.zip |
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Text: |
Code needed to replicate the entire project. |
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Notes: |
application/zip |