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).

Market Area Models for Retail and Service Locations
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.

Estimating the Minimal Clinically Important Difference
c(
person("Zehua", "Zhou", email = "zehuazho@buffalo.edu", role = c("cre","aut")),
person("Jiwei", "Zhao", email = "jiwei.zhao@wisc.edu", role = "aut"))
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>.

Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.

Simultaneous Estimation of the Regression Coefficients and
Precision Matrix
T. Tony Cai, Hongzhe Li, Weidong Liu and Jichun Xie
A robust constrained L1 minimization method for estimating
the regression coefficients and a large sparse inverse
covariance matrix (i.e. precision matrix) simultaneoulsy. The
computation uses linear programming.