  We develop an R package RMM to implement a Conditional Logit (CL) model using
the Robust Demand Estimation (RDE) method introduced in Cho et al. (2020), a
customer choice-based $\textbf{R}$evenue $\textbf{M}$anagement
$\textbf{M}$odel. In business, it is important to understand customers' choice
behavior and preferences when the product prices change over time and across
various customers. However, it is difficult to estimate demand because of
unobservable no-purchase customers (i.e., truncated demand issue). The CL model
fitted using the RDE method, enables a more general utility model with frequent
product price changes. It does not require the aggregation of sales data into
time windows to capture each customer's choice behavior. This study uses real
hotel transaction data to introduce the R package RMM to provide functions that
enable users to fit the CL model using the RDE method along with estimates of
choice probabilities, size of no-purchase customers, and their standard errors.
