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Every week on DartCannon we give a rundown on the week including what we’ve written, are reading, and major imporvements. I won’t cross-post all of them, but will put them up every so often. Happy Friday! Here’s a rundown of this week in DartCannon What We Wrote Accepting an Estimate - DartCannon is about creating simulations of complex business problems, but once the simulation is done the work of getting buy in starts.

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I recently realized that edgarWebR 1.0 was released a while ago without much fanfare. 1.0 is a major milestone for the library, bringing the full set of (initial) planned functionality along with some bonus features. Headline features: 100% coverage of SEC search tools. Parsing of submissions into component files and 10-x filings into items and parts. A dataset of SIC mappings What’s Next: Bugfixes - corner cases keep popping up that need fixing Parsing Improvements - I have some ideas about table handling that will help anyone interested in getting data out of older filings EDGAR Tools The EDGAR System provides a number of tools for filing and entity lookup and examination.

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Last time we made contour maps of densities of points on a globe, now it is time to take another step and make heatmaps. We created all the data we needed when creating the contours, but heatmaps add new challenges of dealing with large amounts of raster and polygon data. Lets get to it. DISCLAIMER: While I know a thing or two, there’s a reasonable chance I got some things wrong or at very least there are certainly more efficient ways to go about things.

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It always happens… I get interested in what I think will be a small data project to scratch some itch and end up down a deep rabbit hole. In this case, a passing interest in the geographic distribution of some samples (more on that in a future post) led to a deep dive into spherical distributions and densities. DISCLAIMER: While I know a thing or two, there’s a reasonable chance I got some things wrong or at very least there are certainly more efficient ways to go about things.

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To understand probability in forecasting, we can take a trip to the grocery store. What We’re Going To Do Demonstrate Estimation by shopping for produce Look at reducing uncertainty Explain reducible vs irreducible uncertainty The Basic Scenario Lets say we’re going to shop for ingredients for a fruit salad consisting of 2 apples, 1 banana and some grapes. Without going any further, you probably can make a reasonable guess about how much things will cost.

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Projects

DartCannon

An online tool for making probability estimates of costs and schedules

edgarWebR

A R Library for accessing and parsing SEC Filings

pds3

A R Library for reading pds3 content