Description: The Multiplicative Competitive Interaction (MCI) Model by Nakanishi & Cooper (1974) <DOI:10.2307/3151146> is an econometric model for analyzing market shares and/or market areas. The functions in this package include fitting the MCI model, MCI shares simulations, the log-centering transformation of MCI datasets, creation of interaction matrices and tools for data preparation.

Description: The Multiplicative Competitive Interaction (MCI) Model by Nakanishi & Cooper (1974) <DOI:10.2307/3151146> is an econometric model for analyzing market shares and/or market areas and is closely related to the Huff model. The functions in this package include fitting the MCI model, MCI shares simulations, the log-centering transformation of MCI datasets, creation of interaction matrices and tools for data preparation. Additionally, the package provides applications for the Huff model, including a non-linear fitting algorithm.

Description: 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).

Title: Market Area Models for Retail and Service Locations
Description: Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package is a more user-friendly wrapper of the functions in the package 'MCI' (Wieland 2017) providing market area analysis using the Huff Model or the Multiplicative Competitive Interaction (MCI) Model. In 'MCI2', also a function for creating transport costs matrices is provided.
Imports: MCI, REAT, tmaptools, osrm, reshape

Title: Estimating the Minimal Clinically Important Difference
person("Zehua", "Zhou", email = "zehuazho@buffalo.edu", role = c("cre","aut")),
person("Jiwei", "Zhao", email = "jiwei.zhao@wisc.edu", role = "aut"))
Description: Apply the marginal classification method to achieve the purpose of providing
the point and interval estimates for the minimal clinically important difference
based on the classical anchor-based method. For more details of the methodology, please
see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.
Packaged: 2021-09-09 00:04:29 UTC; Zehua Zhou