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The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.
Data Types:
  • Software/Code
The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.
Data Types:
  • Software/Code
The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.
Data Types:
  • Software/Code
The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.
Data Types:
  • Software/Code
These files contain Stata and Joinpoint code to run analyses for the paper titled "Impact of public reporting of 30-day mortality on timing of death after coronary artery bypass graft surgery".
Data Types:
  • Software/Code
These files contain Stata code to perform analyses for the manuscript "Validation of the V49.86 Code for Do-Not-Resuscitate Status in Hospitalized Patients at a Single Academic Medical Center".
Data Types:
  • Software/Code
These files contain Stata code to perform analyses for the manuscript "Comparison of Care Patterns and Rehospitalizations for Mechanically Ventilated Patients in New York and Ontario".
Data Types:
  • Software/Code
These files contain Stata code to perform analyses for the manuscript "Derivation of Data-Driven Triggers for Palliative Care Consultation in Critically Ill Patients".
Data Types:
  • Software/Code
Ed is a Jekyll theme designed for textual editors based on minimal computing principles, and focused on legibility, durability, ease and flexibility. One of our most pressing and ever revolving needs as scholars is to pass on our textual artifacts from one generation to another. The art of textual editing, among other practices, has helped many cultures to remember and interpret for centuries. Alas, that art is practiced and encouraged in its highest form by a dwindling number of scholars. In a digital environment the problem is compounded by the difficulties of the medium. While vast repositories, and "e-publications" appear on the online scene yearly, very few manifest a textual scholar's disciplined attention to detail. In contrast, most textual scholars who have made the leap to a rigorous digital practice have focused on markup, relying on technical teams to deploy and maintain their work. This makes your average scholarly digital edition a very costly, and therefore limited affair. As we see it, a minimal edition is one that aims to reduce the size and complexity of the back and front end, and the learning curves for the user and the producer. Out of-the-box, this theme can help you build a simple reading edition, or a traditional scholarly edition with footnotes and a bibliography without breaking the bank. In our estimate, these are the two most immediately useful type of editions for editors and readers. An edition produced with Ed consists of static pages whose rate of decay is substantially lower than database-driven systems. As an added bonus, these static pages require less bandwith. Our hope is that our approach can help beginners or veterans deploy beautiful editions with less effort, that it can help us teach a 'full stack' in one academic semester, allow us to care for our projects at less cost, and perhaps, just perhaps, allow us to generate high-quality editions on github.io in large quantities based on the git-lit model by Jonathan Reeve.
Data Types:
  • Software/Code
The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.
Data Types:
  • Software/Code