Package: turfLP 0.2.0
John Ennis
turfLP: TURF Analysis with Integer Linear Programming
Finds product portfolios that maximize TURF (total unduplicated reach and frequency) with integer linear programming, following Serra (2013) <doi:10.1016/j.foodqual.2012.10.001>. The maximum reach problem is the maximal covering location problem of Church and ReVelle (1974) <doi:10.1007/BF01942293>. The package solves it as an integer linear program, so it finds exact optima without enumerating every portfolio. Ties on reach are broken by frequency and then by the harmonic mean of the individual product reaches. The package also finds the smallest portfolio that reaches every reachable respondent. For related work on TURF for large data sets, see Ennis, Fayle, and Ennis (2012) <doi:10.1016/j.foodqual.2011.06.004>.
Authors:
turfLP_0.2.0.tar.gz
turfLP_0.2.0.zip(r-4.7-any)turfLP_0.2.0.zip(r-4.6-any)turfLP_0.2.0.zip(r-4.5-any)
turfLP_0.2.0.tgz(r-4.6-any)turfLP_0.2.0.tgz(r-4.5-any)
turfLP_0.2.0.tar.gz(r-4.7-any)turfLP_0.2.0.tar.gz(r-4.6-any)
turfLP_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
turfLP/json (API)
| # Install 'turfLP' in R: |
| install.packages('turfLP', repos = c('https://aigorahub.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/aigorahub/turflp/issues
Last updated from:9e6e7e569c. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 202 | ||
| source / vignettes | OK | 152 | ||
| linux-release | OK | 185 | ||
| macos-release | OK | 193 | ||
| macos-oldrel | OK | 182 | ||
| windows-devel | OK | 177 | ||
| windows-release | OK | 166 | ||
| windows-oldrel | OK | 165 | ||
| wasm-release | OK | 83 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simulated cafe drink data | cafe |
| Simulated potato chip purchase intent data | chips |
| Black coffee liking data | coffee |
| Cured ham liking data | ham |
| Simulated ice cream liking data | icecream |
| Lunch bag purchase data | lunchbags |
| Thanksgiving pie data | pies |
| Find the product portfolio with maximum reach | turf |
| Find the smallest portfolio that reaches every reachable respondent | turf_min_cover |
| Simulate a reach matrix | turf_simulate |
| Find the best portfolio for each of several sizes | turf_sizes |