Title: Friendly Input-Output Analysis
Version: 0.1.6
Description: Simplifies the process of importing and managing input-output matrices from 'Microsoft Excel' into R, and provides a suite of functions for analysis. It leverages the 'R6' class for clean, memory-efficient object-oriented programming. Furthermore, all linear algebra computations are implemented in 'Rust' to achieve highly optimized performance.
URL: https://albersonmiranda.github.io/fio/, https://github.com/albersonmiranda/fio
BugReports: https://github.com/albersonmiranda/fio/issues
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 4.2)
SystemRequirements: Cargo (Rust's package manager), rustc >= 1.77, xz
LazyData: true
Imports: cli, clipr, emoji, fs, miniUI, readxl, rlang, shiny, Rdpack (> 2.3.1), R6
License: MIT + file LICENSE
Config/rextendr/version: 0.3.1.9001
Suggests: knitr, rmarkdown, spelling, microbenchmark, leontief, ggplot2, writexl, callr, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Language: en-US
Config/testthat/edition: 3
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2025-04-05 13:50:51 UTC; albersonmiranda
Author: Alberson da Silva Miranda ORCID iD [aut, cre, cph], Celso Bissoli Sessa ORCID iD [dtc]
Maintainer: Alberson da Silva Miranda <albersonmiranda@hotmail.com>
Repository: CRAN
Date/Publication: 2025-04-06 07:50:02 UTC

Brazil input-output matrix, year 2020, 51 sectors

Description

This dataset contains the Brazilian input-output matrix for the year 2020, with 51 sectors. The data is based on the Brazilian Institute of Geography and Statistics (IBGE) and the Brazilian Institute of Applied Economic Research (IPEA).

Usage

br_2020

Format

br_2020

A R6 class containing a set of matrices:

id

Identifier of the new instance

intermediate_transactions

Intermediate transactions matrix.

total_production

Total production matrix.

final_demand

Final demand matrix.

exports

Exports matrix.

imports

Imports matrix.

taxes

Taxes matrix.

value_added

Value added matrix.

Conveniently import data from an Excel file

Description

fio_addin() opens an RStudio gadget and addin that allows you to say where the data source is (either clipboard or Excel file) and import the data into the global environment. Appears as "Import input-output data" in the RStudio Addins menu.

Usage

fio_addin()

References

This function is based on the reprex package.

Import IOM data

Description

Import data from a input-output matrix (IOM) from Excel format.

Usage

import_element(file, sheet, range, col_names = FALSE, row_names = FALSE)

Arguments

file

Path to the Excel file.

sheet

Name of the sheet in the Excel file.

range

Range of cells in the Excel file.

col_names

Range of cells with column names.

row_names

Range of cells with row names.

Value

A (matrix).

Examples

# Excel file with IOM data
path_to_xlsx <- system.file("extdata", "iom/br/2020.xlsx", package = "fio")
# Import IOM data
intermediate_transactions = import_element(
  file = path_to_xlsx,
  sheet = "iom",
  range = "D6:BB56",
  col_names = "D4:BB4",
  row_names = "B6:B56"
)
# Show the first 6 rows and 6 columns
intermediate_transactions[1:6, 1:6]

R6 class for input-output matrix

Description

R6 class for input-output matrix.

Value

A new instance of the iom class.

Public fields

id

(character)
Identifier of the new instance.

intermediate_transactions

(matrix)
Intermediate transactions matrix.

total_production

(matrix)
Total production vector.

household_consumption

(matrix)
Household consumption vector.

government_consumption

(matrix)
Government consumption vector.

exports

(matrix)
Exports vector.

final_demand_others

(matrix)
Other vectors of final demand that doesn't have dedicated slots.

final_demand_matrix

(matrix)
Aggregates final demand vectors into a matrix.

imports

(matrix)
Imports vector.

taxes

(matrix)
Taxes vector.

wages

(matrix)
Wages vector.

operating_income

(matrix)
Operating income vector.

value_added_others

(matrix)
Other vectors of value-added that doesn't have dedicated slots.

value_added_matrix

(matrix)
Aggregates value-added vectors into a matrix.

occupation

(matrix)
Occupation vector.

technical_coefficients_matrix

(matrix)
Technical coefficients matrix.

leontief_inverse_matrix

(matrix)
Leontief inverse matrix.

multiplier_output

(data.frame)
Output multiplier dataframe.

multiplier_employment

(data.frame)
Employment multiplier dataframe.

multiplier_taxes

(data.frame)
Taxes multiplier dataframe.

multiplier_wages

(data.frame)
Wages multiplier dataframe.

field_influence

(matrix)
Influence field matrix.

key_sectors

(data.frame)
Key sectors dataframe.

allocation_coefficients_matrix

(matrix)
Allocation coefficients matrix.

ghosh_inverse_matrix

(matrix)
Ghosh inverse matrix.

hypothetical_extraction

(matrix)
Absolute and relative backward and forward differences in total output after a hypothetical extraction

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Usage
iom$new(
  id,
  intermediate_transactions,
  total_production,
  household_consumption = NULL,
  government_consumption = NULL,
  exports = NULL,
  final_demand_others = NULL,
  imports = NULL,
  taxes = NULL,
  wages = NULL,
  operating_income = NULL,
  value_added_others = NULL,
  occupation = NULL
)
Arguments
id

(character)
Identifier for the input-output matrix.

intermediate_transactions

(matrix)
Intermediate transactions matrix.

total_production

(matrix)
Total production vector.

household_consumption

(matrix)
Household consumption vector.

government_consumption

(matrix)
Government consumption vector.

exports

(matrix)
Exports vector.

final_demand_others

(matrix)
Other vectors of final demand that doesn't have dedicated slots. Setting column names is advised for better readability.

imports

(matrix)
Imports vector.

taxes

(matrix)
Taxes vector.

wages

(matrix)
Wages vector.

operating_income

(matrix)
Operating income vector.

value_added_others

(matrix)
Other vectors of value-added that doesn't have dedicated slots. Setting row names is advised for better readability.

occupation

(matrix)
Occupation matrix.

Method add()

Adds a matrix to the iom object.

Usage
iom$add(matrix_name, matrix)
Arguments
matrix_name

(character)
One of household_consumption, government_consumption, exports, final_demand_others, imports, taxes, wages, operating income, value_added_others or occupation matrix to be added.

matrix

(matrix)
Matrix object to be added.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production)
# Create a dummy matrix
exports_data <- matrix(as.numeric(1:3), 3, 1)
# Add the matrix
my_iom$add("exports", exports_data)

Method remove()

Removes a matrix from the iom object.

Usage
iom$remove(matrix_name)
Arguments
matrix_name

(character)
One of household_consumption, government_consumption, exports, final_demand_others, imports, taxes, wages, operating_income, value_added_others or occupation matrix to be removed.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
 exports_data <- matrix(as.numeric(1:3), 3, 1)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production, exports = exports_data)
# Remove the matrix
my_iom$remove("exports")

Method update_final_demand_matrix()

Aggregates final demand vectors into the final_demand_matrix field.

Usage
iom$update_final_demand_matrix()
Details

Some methods, as ⁠$compute_hypothetical_extraction()⁠, require the final demand and value-added vectors to be aggregated into a matrix. This method does this aggregation, binding the vectors into ⁠$final_demand_matrix⁠.

Returns

This functions doesn't returns a value.

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
# instantiate iom object
my_iom <- iom$new(
 "mock",
 intermediate_transactions,
 total_production,
 exports = exports_data,
 household_consumption = households
)
# aggregate all final demand vectors
my_iom$update_final_demand_matrix()
# check final demand matrix
my_iom$final_demand_matrix

Method update_value_added_matrix()

Aggregates value-added vectors into the value_added_matrix field.

Usage
iom$update_value_added_matrix()
Details

Some methods, as ⁠$compute_hypothetical_extraction()⁠, require the final demand and value-added vectors to be aggregated into a matrix. This method does this aggregation, binding the vectors into ⁠$value_added_matrix⁠.

Returns

This functions doesn't returns a value.

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
imports_data <- matrix(c(5, 10, 15), 1, 3)
taxes_data <- matrix(c(2, 5, 10), 1, 3)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
imports = imports_data,
taxes = taxes_data
)
# aggregate all value-added vectors
my_iom$update_value_added_matrix()
# check value-added matrix
my_iom$value_added_matrix

Method compute_tech_coeff()

Computes the technical coefficients matrix and populate the technical_coefficients_matrix field with the resulting (matrix).

Usage
iom$compute_tech_coeff()
Details

It computes the technical coefficients matrix, a n x n matrix known as A matrix which is the column-wise ratio of intermediate transactions to total production (Leontief 1983).

References

Leontief W (1983). A Economia do Insumo-Produto, Os Economistas. Abril Cultural, São Paulo.

Returns

Self (invisibly).

Examples
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("test", intermediate_transactions, total_production)
# Calculate the technical coefficients
my_iom$compute_tech_coeff()
# show the technical coefficients
my_iom$technical_coefficients_matrix

Method compute_leontief_inverse()

Computes the Leontief inverse matrix and populate the leontief_inverse_matrix field with the resulting (matrix).

Usage
iom$compute_leontief_inverse()
Details

It computes the Leontief inverse matrix (Leontief 1983), which is the inverse of the Leontief matrix, defined as:

L = I - A

where I is the identity matrix and A is the technical coefficients matrix. The Leontief inverse matrix is calculated by solving the following equation:

L^{-1} = (I - A)^{-1}

Since the Leontief matrix is a square matrix and the subtraction of the technical coefficients matrix from the identity matrix guarantees that the Leontief matrix is invertible, underlined Rust function uses LU decomposition to solve the equation.

References:

Leontief W (1983). A Economia do Insumo-Produto, Os Economistas. Abril Cultural, São Paulo.

Returns

Self (invisibly).

Examples
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# show the Leontief inverse
my_iom$leontief_inverse_matrix

Method compute_multiplier_output()

Computes the output multiplier and populate the multiplier_output field with the resulting (data.frame).

Usage
iom$compute_multiplier_output()
Details

An output multiplier for sector j is defined as the total value of production in all sectors of the economy that is necessary in order to satisfy a monetary unit (e.g., a dollar) worth of final demand for sector j's output (Miller and Blair 2009).

This method computes the simple output multiplier, defined as the column sums of the Leontief inverse matrix, the direct and indirect output multipliers, which are the column sums of the technical coefficients matrix and the difference between total and direct output multipliers, respectively (Vale and Perobelli 2020).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the output multiplier
my_iom$compute_multiplier_output()
# show the output multiplier
my_iom$multiplier_output

Method compute_multiplier_employment()

Computes the employment multiplier and populate the multiplier_employment field with the resulting (data.frame).

Usage
iom$compute_multiplier_employment()
Details

The employment multiplier for sector j relates the jobs created in each sector in response to a initial exogenous shock (Miller and Blair 2009).

Current implementation follows (Vale and Perobelli 2020).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
jobs_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, occupation = jobs_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the employment multiplier
my_iom$compute_multiplier_employment()
# show the employment multiplier
my_iom$multiplier_employment

Method compute_multiplier_wages()

Computes the wages multiplier dataframe and populate the multiplier_wages field with the resulting (data.frame).

Usage
iom$compute_multiplier_wages()
Details

The wages multiplier for sector j relates increases in wages for each sector in response to a initial exogenous shock (Miller and Blair 2009).

Current implementation follows (Vale and Perobelli 2020).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
wages_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, wages = wages_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the wages multiplier
my_iom$compute_multiplier_wages()
# show the wages multiplier
my_iom$multiplier_wages

Method compute_multiplier_taxes()

Computes the taxes multiplier and populate the multiplier_taxes field with the resulting (data.frame).

Usage
iom$compute_multiplier_taxes()
Details

The taxes multiplier for sector j relates the increases on tax revenue from each sector in response to a initial exogenous shock (Miller and Blair 2009).

Current implementation follows (Vale and Perobelli 2020).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
tax_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, taxes = tax_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the tax multiplier
my_iom$compute_multiplier_taxes()
# show the taxes multiplier
my_iom$multiplier_taxes

Method compute_field_influence()

Computes the field of influence for all sectors and populate the field_influence field with the resulting (matrix).

Usage
iom$compute_field_influence(epsilon)
Arguments
epsilon

(numeric)
Epsilon value. A technical change in the input-output matrix, caused by a variation of size epsilon into each element of technical coefficients matrix.

Details

The field of influence shows how changes in direct coefficients are distributed throughout the entire economic system, allowing for the determination of which relationships between sectors are most important within the production process.

It determines which sectors have the greatest influence over others, specifically, which coefficients, when altered, would have the greatest impact on the system as a whole (Vale and Perobelli 2020).

References

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate field of influence
my_iom$compute_field_influence(epsilon = 0.01)
# show the field of influence
my_iom$field_influence

Method compute_key_sectors()

Computes the key sectors dataframe, based on it's power and sensitivity of dispersion, and populate the key_sectors field with the resulting (data.frame).

Usage
iom$compute_key_sectors()
Details

Increased production from a sector j means that the sector j will need to purchase more goods from other sectors. At the same time, it means that more goods from sector j will be available for other sectors to purchase. Sectors that are above average in the demand sense (stronger backward linkage) have power of dispersion indices greater than 1. Sectors that are above average in the supply sense (stronger forward linkage) have sensitivity of dispersion indices greater than 1 (Miller and Blair 2009).

As both power and sensitivity of dispersion are related to average values on the economy, coefficients of variation are also calculated for both indices. The lesser the coefficient of variation, greater the number of sectors on the demand or supply structure of that sector (Vale and Perobelli 2020).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Vale VdA, Perobelli FS (2020). Análise de Insumo-Produto: teoria e aplicações no R. Edição Independente, Curitiba, PR. ISBN 9786500103649.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate key sectors
my_iom$compute_key_sectors()
# show the key sectors
my_iom$key_sectors

Method compute_allocation_coeff()

Computes the allocation coefficients matrix and populate the allocation_coefficients_matrix field with the resulting (matrix).

Usage
iom$compute_allocation_coeff()
Details

It computes the allocation coefficients matrix, a n x n matrix known as B matrix which is the row-wise ratio of intermediate transactions to total production (Miller and Blair 2009).

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Returns

Self (invisibly).

Examples
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# show the allocation coefficients
my_iom$allocation_coefficients_matrix

Method compute_ghosh_inverse()

Computes the Ghosh inverse matrix and populate the ghosh_inverse_matrix field with the resulting (matrix).

Usage
iom$compute_ghosh_inverse()
Details

It computes the Ghosh inverse matrix (Miller and Blair 2009), defined as:

G = (I - B)^{-1}

where I is the identity matrix and B is the allocation coefficients matrix.

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Returns

Self (invisibly).

Examples
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# Calculate the Ghosh inverse
my_iom$compute_ghosh_inverse()
# show the Ghosh inverse
my_iom$ghosh_inverse_matrix

Method compute_hypothetical_extraction()

Computes total impact after extracting a each sector and populate the hypothetical_extraction field with the resulting (data.frame).

Usage
iom$compute_hypothetical_extraction()
Details

Computes impact on demand and supply structures after extracting each sector (Miller and Blair 2009).

The total impact is calculated by the sum of the direct and indirect impacts.

References

Miller RE, Blair PD (2009). Input-Output Analysis: Foundations and Extensions, 2 edition. Cambridge University Press. ISBN 978-0-521-73902-3 978-0-521-51713-3 978-0-511-62698-2, doi:10.1017/CBO9780511626982, https://www.cambridge.org/core/product/identifier/9780511626982/type/book.

Returns

Self (invisibly).

Examples
# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(5, 10, 15), 3, 1)
holsehold_consumption_data <- matrix(c(20, 25, 30), 3, 1)
operating_income_data <- matrix(c(2, 5, 10), 1, 3)
taxes_data <- matrix(c(1, 2, 3), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new(
 "test",
 intermediate_transactions,
 total_production,
 exports = exports_data,
 household_consumption = holsehold_consumption_data,
 operating_income = operating_income_data,
 taxes = taxes_data
)
# update value-added matrix
my_iom$update_value_added_matrix()
# update final demand matrix
my_iom$update_final_demand_matrix()
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate allocation coefficients
my_iom$compute_allocation_coeff()
# calculate Ghosh inverse
my_iom$compute_ghosh_inverse()
# calculate hypothetical extraction
my_iom$compute_hypothetical_extraction()
# show results
my_iom$hypothetical_extraction

Method set_max_threads()

Sets max number of threads used by fio and populate the threads field with the resulting (integer).

Usage
iom$set_max_threads(max_threads)
Arguments
max_threads

(integer)
Number of threads enabled for parallel computing. Defaults to 0, meaning all threads available.

Details

Calling this function sets a global limit of threads to Rayon crate, affecting all computations that runs in parallel by default.

Default behavior of Rayon is to use all available threads (including logical). Setting to 1 will result in single threaded (sequential) computations.

Initialization of the global thread pool happens exactly once. Once started, the configuration cannot be changed in the current session. If ⁠$set_max_threads()⁠ is called again in the same session, it'll result in an error.

Methods that deals with linear algebra computations, like ⁠$compute_leontief_inverse()⁠ and ⁠$compute_ghosh_inverse()⁠, will try to use all available threads by default, so they also initializes global thread pool. In order to choose a maximum number of threads other than default, ⁠$set_max_threads()⁠ must be called before any computation, preferably right after iom$new().

Returns

This function does not return a value.

Examples
\dontrun{
 intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
 total_production <- matrix(c(100, 200, 300), 1, 3)
 # instantiate iom object
 my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
 # to run single threaded (sequential)
 my_iom$set_max_threads(1L)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
iom$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
imports <- matrix(c(5, 10, 15), 1, 3)
jobs <- matrix(c(10, 12, 15), 1, 3)
taxes <- matrix(c(2, 5, 10), 1, 3)
wages <- matrix(c(11, 12, 13), 1, 3)

# a new iom instance can be created by passing just intermediate transactions and total production
my_iom <- iom$new(
 "example_1",
 intermediate_transactions,
 total_production
)

# or by passing optional arguments
my_iom <- iom$new(
"example_2",
intermediate_transactions,
total_production,
household_consumption = households,
exports = exports,
imports = imports,
taxes = taxes,
wages = wages,
occupation = jobs
)


## ------------------------------------------------
## Method `iom$add`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production)
# Create a dummy matrix
exports_data <- matrix(as.numeric(1:3), 3, 1)
# Add the matrix
my_iom$add("exports", exports_data)

## ------------------------------------------------
## Method `iom$remove`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
 exports_data <- matrix(as.numeric(1:3), 3, 1)
# instantiate iom object
my_iom <- iom$new("mock", intermediate_transactions, total_production, exports = exports_data)
# Remove the matrix
my_iom$remove("exports")

## ------------------------------------------------
## Method `iom$update_final_demand_matrix`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(10, 20, 30), 3, 1)
households <- matrix(as.numeric(4:6), 3, 1)
# instantiate iom object
my_iom <- iom$new(
 "mock",
 intermediate_transactions,
 total_production,
 exports = exports_data,
 household_consumption = households
)
# aggregate all final demand vectors
my_iom$update_final_demand_matrix()
# check final demand matrix
my_iom$final_demand_matrix

## ------------------------------------------------
## Method `iom$update_value_added_matrix`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
imports_data <- matrix(c(5, 10, 15), 1, 3)
taxes_data <- matrix(c(2, 5, 10), 1, 3)
# instantiate iom object
my_iom <- iom$new(
"mock",
intermediate_transactions,
total_production,
imports = imports_data,
taxes = taxes_data
)
# aggregate all value-added vectors
my_iom$update_value_added_matrix()
# check value-added matrix
my_iom$value_added_matrix

## ------------------------------------------------
## Method `iom$compute_tech_coeff`
## ------------------------------------------------

intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- iom$new("test", intermediate_transactions, total_production)
# Calculate the technical coefficients
my_iom$compute_tech_coeff()
# show the technical coefficients
my_iom$technical_coefficients_matrix

## ------------------------------------------------
## Method `iom$compute_leontief_inverse`
## ------------------------------------------------

intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# show the Leontief inverse
my_iom$leontief_inverse_matrix

## ------------------------------------------------
## Method `iom$compute_multiplier_output`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the output multiplier
my_iom$compute_multiplier_output()
# show the output multiplier
my_iom$multiplier_output

## ------------------------------------------------
## Method `iom$compute_multiplier_employment`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
jobs_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, occupation = jobs_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the employment multiplier
my_iom$compute_multiplier_employment()
# show the employment multiplier
my_iom$multiplier_employment

## ------------------------------------------------
## Method `iom$compute_multiplier_wages`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
wages_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, wages = wages_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the wages multiplier
my_iom$compute_multiplier_wages()
# show the wages multiplier
my_iom$multiplier_wages

## ------------------------------------------------
## Method `iom$compute_multiplier_taxes`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
tax_data <- matrix(c(10, 12, 15), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production, taxes = tax_data)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate the tax multiplier
my_iom$compute_multiplier_taxes()
# show the taxes multiplier
my_iom$multiplier_taxes

## ------------------------------------------------
## Method `iom$compute_field_influence`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate field of influence
my_iom$compute_field_influence(epsilon = 0.01)
# show the field of influence
my_iom$field_influence

## ------------------------------------------------
## Method `iom$compute_key_sectors`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate key sectors
my_iom$compute_key_sectors()
# show the key sectors
my_iom$key_sectors

## ------------------------------------------------
## Method `iom$compute_allocation_coeff`
## ------------------------------------------------

intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# show the allocation coefficients
my_iom$allocation_coefficients_matrix

## ------------------------------------------------
## Method `iom$compute_ghosh_inverse`
## ------------------------------------------------

intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
# Calculate the allocation coefficients
my_iom$compute_allocation_coeff()
# Calculate the Ghosh inverse
my_iom$compute_ghosh_inverse()
# show the Ghosh inverse
my_iom$ghosh_inverse_matrix

## ------------------------------------------------
## Method `iom$compute_hypothetical_extraction`
## ------------------------------------------------

# data
intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
total_production <- matrix(c(100, 200, 300), 1, 3)
exports_data <- matrix(c(5, 10, 15), 3, 1)
holsehold_consumption_data <- matrix(c(20, 25, 30), 3, 1)
operating_income_data <- matrix(c(2, 5, 10), 1, 3)
taxes_data <- matrix(c(1, 2, 3), 1, 3)
# instantiate iom object
my_iom <- fio::iom$new(
 "test",
 intermediate_transactions,
 total_production,
 exports = exports_data,
 household_consumption = holsehold_consumption_data,
 operating_income = operating_income_data,
 taxes = taxes_data
)
# update value-added matrix
my_iom$update_value_added_matrix()
# update final demand matrix
my_iom$update_final_demand_matrix()
# calculate the technical coefficients
my_iom$compute_tech_coeff()
# calculate the Leontief inverse
my_iom$compute_leontief_inverse()
# calculate allocation coefficients
my_iom$compute_allocation_coeff()
# calculate Ghosh inverse
my_iom$compute_ghosh_inverse()
# calculate hypothetical extraction
my_iom$compute_hypothetical_extraction()
# show results
my_iom$hypothetical_extraction

## ------------------------------------------------
## Method `iom$set_max_threads`
## ------------------------------------------------

## Not run: 
 intermediate_transactions <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), 3, 3)
 total_production <- matrix(c(100, 200, 300), 1, 3)
 # instantiate iom object
 my_iom <- fio::iom$new("test", intermediate_transactions, total_production)
 # to run single threaded (sequential)
 my_iom$set_max_threads(1L)

## End(Not run)