Package: MCI 1.3.3

Thomas Wieland

MCI: Multiplicative Competitive Interaction (MCI) Model

Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).

Authors:Thomas Wieland

MCI_1.3.3.tar.gz
MCI_1.3.3.tar.gz(r-4.5-noble)MCI_1.3.3.tar.gz(r-4.4-noble)
MCI_1.3.3.tgz(r-4.4-emscripten)MCI_1.3.3.tgz(r-4.3-emscripten)
MCI.pdf |MCI.html
MCI/json (API)

# Install 'MCI' in R:
install.packages('MCI', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • DIY1 - Distance matrix for DIY stores
  • DIY2 - DIY store information
  • DIY3 - Data for origins
  • Freiburg1 - Distance matrix for grocery stores in Freiburg
  • Freiburg2 - Statistical districts of Freiburg
  • Freiburg3 - Grocery stores in Freiburg
  • grocery1 - Grocery store choices in Goettingen
  • grocery2 - Grocery store market areas in Goettingen
  • shopping1 - Point-of-sale survey in Karlsruhe
  • shopping2 - Distance matrix for the point-of-sale survey in Karlsruhe
  • shopping3 - Market area data for the point-of-sale survey in Karlsruhe
  • shopping4 - Grocery store data for the point-of-sale survey in Karlsruhe

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.53 score 42 scripts 180 downloads 8 mentions 20 exports 0 dependencies

Last updated 7 years agofrom:2b459b2deb. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 19 2024
R-4.5-linuxOKDec 19 2024

Exports:geomhuff.attrachuff.decayhuff.fithuff.lambdahuff.sharesijmatrix.createijmatrix.crosstabijmatrix.shareslm.betamci.fitmci.sharesmci.shares.elastmci.transmatmci.transvarmodel.fitshares.segmshares.totalvar.asdummyvar.correct

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Multiplicative Competitive Interaction (MCI) ModelMCI-package MCI
Distance matrix for DIY storesDIY1
DIY store informationDIY2
Data for origins (DIY store customers' places of residence)DIY3
Distance matrix for grocery stores in FreiburgFreiburg1
Statistical districts of FreiburgFreiburg2
Grocery stores in FreiburgFreiburg3
Geometric meangeom
Grocery store choices in Goettingengrocery1
Grocery store market areas in Goettingengrocery2
Local optimization of attraction values in the Huff Modelhuff.attrac
Distance decay function in the Huff modelhuff.decay
Fitting the Huff model using local optimization of attractivityhuff.fit
Fitting the distance parameter lambda in the Huff modelhuff.lambda
Huff model market share/market area simulationshuff.shares
Interaction matrix with market sharesijmatrix.create
Converting interaction matrix with market shares to crosstableijmatrix.crosstab
Market shares in interaction matrixijmatrix.shares
Beta regression coefficientslm.beta
Fitting the MCI modelmci.fit
MCI market share/market area simulationsmci.shares
Market share elasticitiesmci.shares.elast
Log-centering transformation of an MCI interaction matrixmci.transmat
Log-centering transformation of one variable in an MCI interaction matrixmci.transvar
Goodness of fit statistics for the Huff modelmodel.fit
Segmentation of market areas by a criterionshares.segm
Total market shares/market areasshares.total
Point-of-sale survey in Karlsruheshopping1
Distance matrix for the point-of-sale survey in Karlsruheshopping2
Market area data for the point-of-sale survey in Karlsruheshopping3
Grocery store data for the point-of-sale survey in Karlsruheshopping4
Creating dummy variablesvar.asdummy
Correcting MCI input variablesvar.correct