Composite Indicator Construction and Analysis
Impute by group mean
Impute by median
Impute by group median
Convert iCodes to iNames
Log transform a vector (skew corrected)
Log-transform a vector
Normalise using Borda scores
Normalise as distance to maximum value
Normalise as distance to reference value
Z-score a vector
Generate short codes from long names
Create a new coin
Create a normalised data set
Normalise a data frame
Log-transform a vector
Weighted harmonic mean
Aggregate indicators in a coin
Aggregate data frame
Compound annual growth rate
Weighted arithmetic mean
Copeland scores
Weighted generalised mean
Weighted geometric mean
Get correlations
Aggregate indicators
Aggregate data
Interpolate time-indexed data frame
Box Cox transformation
Build ASEM example coin
Build example purse
Add and remove indicators
Check iData
Check iMeta
Check skew and kurtosis of a vector
Convert a COIN to a coin
Find highly-correlated indicators within groups
Compare two coins
Compare two coins by correlation
Compare multiple coins
Compare two data frames
Custom operation
Custom operation
Custom operation
Denominate data set in a coin
Denominate data sets by other variables
Denominate a data set within a purse.
Denominate data
Export a coin to Excel
Export a purse to Excel
Export a coin or purse to Excel
Cronbach's alpha
Get subsets of indicator data
Get subsets of indicator data
Get subsets of indicator data
Get data availability of units
Get data availability of units
Get data availability of units
Correlations between indicators and denominators
Gets a named data set and performs checks
Gets a named data set and performs checks
Gets a named data set and performs checks
Get effective weights
Noisy replications of weights
Weight optimisation
Perform PCA on a coin
P-values for correlations in a data frame or matrix
Results summary tables
Sensitivity and uncertainty analysis of a coin
Log-transform a vector
Statistics of indicators
Statistics of columns
Statistics of columns/indicators
Generate strengths and weaknesses for a specified unit
Get time trends
Generate unit summary table
Impute by mean
Import data directly from COIN Tool
Impute a data set in a coin
Impute a data frame
Impute a numeric vector
Impute data sets in a purse
Imputation of missing data
Impute panel data
Check if object is coin class
Check if object is purse class
Calculate kurtosis
Normalise as distance to target
Normalise as fraction of max value
Normalise using goalpost method
Minmax a vector
Normalise using percentile ranks
Normalise using ranks
Scale a vector
Normalise a numeric vector
Create normalised data sets in a purse of coins
Normalise data
Outranking matrix
Bar chart
Static heatmaps of correlation matrices
Convert a data frame to ranks
Static indicator distribution plots
Dot plots of single indicator with highlighting
Framework plots
Scatter plot of two variables
Plot sensitivity indices
Convert uCodes to uNames
Plot ranks from an uncertainty/sensitivity analysis
Percentage change of time series
Print coin
Print purse
Quick normalisation of a coin
Quick normalisation of a data frame
Quick normalisation of a purse
Quick normalisation
Quick outlier treatment of a coin
Quick outlier treatment of a data frame
Quick outlier treatment of a purse
Quick outlier treatment
Regenerate a coin
Regenerate a purse
Regenerate a coin or purse
Check the effect of removing indicators or aggregates
Replace multiple values in a data frame
Round down a data frame
Estimate sensitivity indices
Generate sample for sensitivity analysis
Screen units based on data availability
Screen units based on data availability
Screen units based on data availability
Screen units based on data availability
Round a data frame to specified significant figures
Calculate skewness
Treat a data set in a coin for outliers
Treat a data frame for outliers
Treat a numeric vector for outliers
Treat a purse of coins for outliers
Treat outliers
Winsorise a vector
A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.