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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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PDS3 is a data standard used extensively by NASA for archiving data from science missions, maintained by JPL. While being replaced by PDS4, PDS4 covers all currently active missions and those covering the history of US space exploration. The R pds3 package provides tools for parsing PDS3 data, particularly the ODL label format which describes all the metadata of data collection. Want to plot a heatmap of Mars of all the images taken?

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We’re so exited to see the response to our launch, we wanted to let you know what you can expect from us in the coming months. To fulfill out mission of bringing advanced tools to leaders at all levels, we launched with just the basics in place and have so much more planned. While we can’t commit to a specific date, here is some of what we’re working on:

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Most estimation is taking a single shot in the dark. DartCannon exists to let you take thousands of shots on the goal – firing as many darts at the dart board as needed to get an understanding of where they’ll fall. Previously this capability was limited to planning departments with deep pockets, willing to shell out for esoteric, complicated pieces of software. DartCannon changes the game, bringing those advanced tools to a price point and simplicity where anyone can use them.

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