compute_CI

Diagnosis of collinearity in matrix <code>X</code>

collinearity

ConditionIndex

Different functions includes constructing composite indicators, imputing missing data, and evaluating imputation techniques. Additionally, different tools for data normalization. Detailed methodologies of 'Indicator' package are: OECD/European Union/EC-JRC (2008), "Handbook on Constructing Composite Indicators: Methodology and User Guide", OECD Publishing, Paris, <DOI:10.1787/533411815016>, Matteo Mazziotta & Adriano Pareto, (2018) "Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices" <DOI:10.1007/s11205-017-1577-5> and De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices of Development and Poverty: An Application to MDGs" <DOI:10.1007/s11205-010-9727-z>.

Gianmarco Borrata

Indicator

Composite 'Indicator' Construction and Imputation Data

Pasquale Pipiciello

compute_CI function

<dl><dt>matrix</dt>
<dd>a matrix of data where rows = observations and columns =
variables</dd></dl>

Arguments

Calculation of Condition Indices — compute_CI

<dl>

<dt>matrix</dt>
<dd>a matrix of data where rows = observations and columns =
variables</dd>

</dl>

compute_CI: Calculation of Condition Indices

Description

Usage

Value

Arguments

Details

References

Examples