Download JAR files tagged by impact with all dependencies

The purpose of this module is solely for building aggregated clover test coverage reports. It is for internal Stormpath use and requires a license. It is only ever used with the clover-it profile and should not impact the public's ability to build the stormpath-sdk-java project

Java SDK for the Load Impact API. The primary class to use is com.loadimpact.ApiTokenClient. This SDK is closely modeled after the API described at Load Impact - Platform API Reference (http://developers.loadimpact.com/api/index.html) View the JavaDocs for a full description of every operation.

Exclude files from analysis based on a cutoff date threshold. Any source file that was last modified prior to the configured cutoff date is excluded. This can be useful when you want to analyze the work done on an existing code base and measure the quality of new code/changes only. This way previous phases of the project will not impact the analysis.

This module groups all different output writers. Different output writers have different dependencies. To ensure that those dependencies do not leak to other output writer, or that we can limit to number of dependencies at runtime, output writers are split into separate modules based on the dependencies they use. For example, the CloudWatch output writers depends on the AWS SDK, which is quite large and not used by any other output writer. If you only want to send metrics to Graphite, there should be no need to have the AWS SDK in your classpath at runtime. Some dependencies are also problematic. They can introduce incompatibilities. For example, a few writers are based directly on log4j or logback. This direct dependence breaks the use of SLF4J. By isolating those output writers to a specific module, we can ensure this has minimal impact.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616 This version allows defining section scopes for sentence segmentation.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616