
zipfr.r-forge.r-project.org
The zipfR Package HomepageInformation about the zipfR package to study lexical frequency distributions in R
http://zipfr.r-forge.r-project.org/
Information about the zipfR package to study lexical frequency distributions in R
http://zipfr.r-forge.r-project.org/
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The zipfR Package Homepage | zipfr.r-forge.r-project.org Reviews
https://zipfr.r-forge.r-project.org
Information about the zipfR package to study lexical frequency distributions in R
Excel Tricks – PoliceAnalyst.com
http://policeanalyst.com/category/excel-tricks
Using SUBTOTAL for Dynamic Sums. This is a quick trick, but a real timesaver. Every day I encounter the situation where I have a filtered list of data and I want to sum up one of the columns. A simple example can be seen in the screen grab below, where I have some fictional officers across four platoons with their monthly ticket totals. Whoops, even though I filtered the Unit column down to just Platoon 1, the sum remains the same. You may be asking Why the 9? Get used to always using SUBTOTAL. If yo...
Comparing Event Counts with the Poisson Means Test – PoliceAnalyst.com
http://policeanalyst.com/comparing-event-counts-with-the-poisson-means-test
Comparing Event Counts with the Poisson Means Test. An exceedingly common task for crime analysts is to count the number of events that occur in one time period (e.g. day, week, month, YTD) and compare them to a prior time period. The change between these two numbers if often the source of much strategy and project-making in law enforcement. What does it mean that we had more motor vehicle collisions this week compared to last? What are we doing right to make robberies go down this month? Where he explic...
Analysis Tools – PoliceAnalyst.com
http://policeanalyst.com/category/analysis-tools
Using SUBTOTAL for Dynamic Sums. This is a quick trick, but a real timesaver. Every day I encounter the situation where I have a filtered list of data and I want to sum up one of the columns. A simple example can be seen in the screen grab below, where I have some fictional officers across four platoons with their monthly ticket totals. Whoops, even though I filtered the Unit column down to just Platoon 1, the sum remains the same. You may be asking Why the 9? Get used to always using SUBTOTAL. If yo...
Stats – PoliceAnalyst.com
http://policeanalyst.com/tag/stats
Comparing Event Counts with the Poisson Means Test. An exceedingly common task for crime analysts is to count the number of events that occur in one time period (e.g. day, week, month, YTD) and compare them to a prior time period. The change between these two numbers if often the source of much strategy and project-making in law enforcement. What does it mean that we had more motor vehicle collisions this week compared to last? What are we doing right to make robberies go down this month? Where he explic...
Complex Adaptive Systems: December 2009
http://coadsy.blogspot.com/2009_12_01_archive.html
Sunday, 13 December 2009. Last friday I gave presentation on Zipf's law. Primarily concerned with processing frequency lists and spectra in R. The scripts contain two Python programs for extracting frequencies out of NLTK. S internal Gutenberg selection corpus and the section J of the ACL Anthology. Corpus. If you don't have access to the ACL, I provide the processed TFL and SPC files for both corpora in the ZIP file. Subscribe to: Posts (Atom). Follow me on Twitter. View my complete profile.
Complex Adaptive Systems: Zipf's law
http://coadsy.blogspot.com/2009/12/zipfs-law.html
Sunday, 13 December 2009. Last friday I gave presentation on Zipf's law. Primarily concerned with processing frequency lists and spectra in R. The scripts contain two Python programs for extracting frequencies out of NLTK. S internal Gutenberg selection corpus and the section J of the ACL Anthology. Corpus. If you don't have access to the ACL, I provide the processed TFL and SPC files for both corpora in the ZIP file. Subscribe to: Post Comments (Atom). Follow me on Twitter. View my complete profile.
Software and Resources
http://www.stefan-evert.de/Software.html
Bull; Corpus Linguistics. Bull; FAU Erlangen-Nürnberg. ZipfR - LNRE models of word frequency distributions in R. Is a library for the statistical computing environment R. That implements several statistical models for the distribution of word frequencies in natural language. These models can also be applied to many other linguistic and non-linguistic quantities and are often called LNRE models. For large number of rare events. Since its first public release in summer 2006, zipfR. Is a collection of Perl ...
Best Practices – PoliceAnalyst.com
http://policeanalyst.com/category/best-practices
Halton Regional Police Service wins 2016 ComputerWorld Data Editors’ Choice Award. In the September issue of ComputerWorld the Halton Regional Police Service was awarded a 2016 Data Editors’ Choice Award for their innovative use of data and analytics to improve the effectiveness and efficiency of the police service. For all the talk of big data and technology, I think this is the most important insight for organizations looking to adopt data analytics:. October 2, 2016. October 2, 2016. These types of qu...
Poisson – PoliceAnalyst.com
http://policeanalyst.com/tag/poisson
Comparing Event Counts with the Poisson Means Test. An exceedingly common task for crime analysts is to count the number of events that occur in one time period (e.g. day, week, month, YTD) and compare them to a prior time period. The change between these two numbers if often the source of much strategy and project-making in law enforcement. What does it mean that we had more motor vehicle collisions this week compared to last? What are we doing right to make robberies go down this month? Where he explic...
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The zipfR Package Homepage
User-friendly LNRE modelling in R. Counting Words (ESSLLI course). Contact information and mailing list. Current version: 0.6-5. Mdash; now on R-Forge. Environment for statistical computing. Like R, zipfR. A few examples of application areas where the zipfR. Functionalities can be useful:. Investigating the quantitative productivity of word formation processes and other linguistic phenomena. Providing empirically supported Bayesian priors for lexical probabilities. Is being developed by Stefan Evert.
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