banks

<p>The dataset consists of 31 banks operating in Taiwan.</p>

datasets

Includes functions to estimate production frontiers
and make ideal output predictions in the Data Envelopment Analysis (DEA)
context using both standard models from DEA and Free Disposal Hull (FDH)
and boosting techniques. In particular, EATBoosting (Guillen et al., 2023
<doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package
includes code for estimating several technical efficiency measures using
different models such as the input and output-oriented radial measures, the
input and output-oriented Russell measures, the Directional Distance
Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based
Measure (SBM).

Maria D. Guillen

boostingDEA

A Boosting Approach to Data Envelopment Analysis

Juan Aparicio

Víctor España

banks function

<p><code>banks</code> is a dataframe with 31 banks (rows) and 6 variables
(outputs) named <code>Financial.funds</code> (deposits and borrowed funds in
millions of TWD), <code>Labor</code> (number of employees),
<code>Physical.capital</code> (net amount of fixed assets in millions of TWD),
<code>Finalcial.investments</code> (financial assets, securities, and equity
investments in millions of TWD), <code>Loans</code> (loans and discounts in millions
of TWD) and <code>Revenue</code> (interests from financial investments and loans).</p>

banks: Taiwanese banks (in 2010)

Description

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