Constructs and analyzes optimal row-column designs for mixed-level factorial experiments under two field situations like square field layouts, where the number of rows and columns are equal, and rectangular field layouts, where one blocking direction is determined by a selected block size. For square field layouts, the package implements direct common-factor constructions by first forming two component treatment arrays, one for each factor or super-factor, and then combining them through a symbolic cell-wise product following Gopinath, Parsad and Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular field layouts, the package constructs designs by extracting a balanced principal block from a mixed-level block design derived from r package mixedfact, treating it as the principal column, taking the complete treatment set as the principal row, and generating the full row-column design by cyclic modular development. The package also provides diagnostic tools for connectedness, orthogonal factorial structure, balance, estimability, A-, D-, and E-efficiency, E-optimality, and MV-optimality criterion values. These designs are practically useful in agricultural, industrial, and biological experiments where treatments have factorial structure and heterogeneity must be controlled simultaneously in two directions.