Introduction | A Traditional Approach | A More Flexible and Robust Approach | Notes on the Workflow | About MarketMatching Package | Summary of features: | Example | Prospective Pseudo Power Curves | Getting optimized market pairs (test/control) recommendations | How Does Dynamic Time Warping Work? | Dynamic Time Warping Example | How Does Intervention Inference Work? | Some Technical Details | About Spike-and-Slab Priors | How to Select the Local Level Standard Error | Note on Structural Time Series Models | References
Package: MarketMatching 1.2.1
MarketMatching: Market Matching and Causal Impact Inference
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
Authors:
MarketMatching_1.2.1.tar.gz
MarketMatching_1.2.1.zip(r-4.7)MarketMatching_1.2.1.zip(r-4.6)MarketMatching_1.2.1.zip(r-4.5)
MarketMatching_1.2.1.tgz(r-4.6-any)MarketMatching_1.2.1.tgz(r-4.5-any)
MarketMatching_1.2.1.tar.gz(r-4.7-any)MarketMatching_1.2.1.tar.gz(r-4.6-any)
MarketMatching_1.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MarketMatching/json (API)
| # Install 'MarketMatching' in R: |
| install.packages('MarketMatching', repos = c('https://klarsen1.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/klarsen1/marketmatching/issues
Datasets:
- weather - Weather dataset
Last updated from:85cdb5bbd2. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 148 | ||
| source / vignettes | OK | 307 | ||
| linux-release-x86_64 | OK | 153 | ||
| macos-release-arm64 | OK | 86 | ||
| macos-oldrel-arm64 | OK | 98 | ||
| windows-devel | OK | 101 | ||
| windows-release | OK | 104 | ||
| windows-oldrel | OK | 100 | ||
| wasm-release | OK | 135 |
Exports:best_matchesinferenceroll_up_optimal_pairstest_fake_lift
Dependencies:assertthatBoomBoomSpikeSlabbstsCausalImpactclicodetoolscpp11doParalleldplyrdtwfarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSpillarpkgconfigplyrproxypurrrR6RColorBrewerRcppreshape2rlangS7scalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxtszoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| For each market, find the best matching control market | best_matches |
| Given a test market, analyze the impact of an intervention | inference |
| Market Matching and Causal Impact Inference | MarketMatching-package MarketMatching |
| Roll up the suggested test/control optimal pairs for pseudo power analysis (testing fake lift) | roll_up_optimal_pairs |
| Given a test market, analyze the impact of fake interventions (prospective power analysis) | test_fake_lift |
| Weather dataset | weather |
