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Statement of need

Matrices are important mathematical objects, and they often describe networks of flows among nodes. Example networks are given in the following table. The power of matrices lies in their ability to organize network-wide calculations, thereby simplifying the work of analysts who study entire systems. However, three problems arise when performing matrix operations in R and other languages. The R package matsbyname enables matrix mathematics wherein operations are performed “by name” and row and column types are allowed.

Problem 1

Although built-in matrix functions ensure size conformity of matrix operands, they do not respect the names of rows and columns (known as dimnames in R). If the rows and columns are not in the same order, mathematical operations with matrices are nonsensical.

Problem 2

In many cases, operand matrices may have different numbers or different names of rows or columns. This situation can occur when, for example, products or industries changes across time periods. When performing matrix operations, rows or columns of zeros must be added to ensure name conformity. The analyst’s burden is cumbersome. But worse problems await. Respecting names (and adding rows and columns of zeroes) can lead to an inability to invert matrices downstream.

Problem 3

Matrix functions provided by R and other languages do not ensure type conformity for matrix operands to matrix algebra functions. In the example of matrix multiplication, columns of the multiplicand must contain the same type of information as the as the rows of the multiplier. If the columns of A are countries, then the rows of B must also be countries (and in the same order) if A %*% B is to make sense.

This package provides functions that respect row and column names when performing matrix mathematics in R. Furthermore, operations can be performed on lists of matrices, such as columns in a matsindf data frame.


You can install matsbyname from CRAN with:


You can install a recent development version of matsbyname from github with:

# install devtools if not already installed
# install.packages("devtools")
# To build vignettes locally, use
devtools::install_github("MatthewHeun/matsbyname", build_vignettes = TRUE)


The functions in this package were used in Heun et al. (2018).

More Information

Find more information, including vignettes and function documentation, at


Heun, Matthew Kuperus, Anne Owen, and Paul E. Brockway. 2018. “A Physical Supply-Use Table Framework for Energy Analysis on the Energy Conversion Chain.” Applied Energy 226 (September): 1134–62.