Tools to build and work with bilateral generalized-mean price indexes (and by extension quantity indexes), and indexes composed of generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS). Covers the core mathematical machinery for making bilateral price indexes, computing price relatives, detecting outliers, and decomposing indexes, with wrappers for all common (and many uncommon) index-number formulas. Implements and extends many of the methods in Balk (2008), von der Lippe (2007), and the CPI manual (2020).

Get the stable release from CRAN.

```
install.packages("gpindex")
```

The development version can be installed from R-Universe

```
install.packages("gpindex", repos = c("https://marberts.r-universe.dev", "https://cloud.r-project.org"))
```

or directly from GitHub.

```
pak::pak("marberts/gpindex")
```

```
library(gpindex)
# Start with some data on prices and quantities for 6 products
# over 5 periods
price6
#> t1 t2 t3 t4 t5
#> 1 1 1.2 1.0 0.8 1.0
#> 2 1 3.0 1.0 0.5 1.0
#> 3 1 1.3 1.5 1.6 1.6
#> 4 1 0.7 0.5 0.3 0.1
#> 5 1 1.4 1.7 1.9 2.0
#> 6 1 0.8 0.6 0.4 0.2
quantity6
#> t1 t2 t3 t4 t5
#> 1 1.0 0.8 1.0 1.2 0.9
#> 2 1.0 0.9 1.1 1.2 1.2
#> 3 2.0 1.9 1.8 1.9 2.0
#> 4 1.0 1.3 3.0 6.0 12.0
#> 5 4.5 4.7 5.0 5.6 6.5
#> 6 0.5 0.6 0.8 1.3 2.5
# We'll only need prices and quantities for a few periods
p0 <- price6[[1]]
p1 <- price6[[2]]
p2 <- price6[[3]]
q0 <- price6[[1]]
q1 <- price6[[2]]
# There are functions to calculate all common price indexes,
# like the Laspeyres and Paasche index
laspeyres_index(p1, p0, q0)
#> [1] 1.4
paasche_index(p1, p0, q1)
#> [1] 1.811905
# The underlying mean functions are also available, as usually
# only price relatives and weights are known
s0 <- p0 * q0
s1 <- p1 * q1
arithmetic_mean(p1 / p0, s0)
#> [1] 1.4
harmonic_mean(p1 / p0, s1)
#> [1] 1.811905
# The mean representation of a Laspeyres index makes it easy to
# chain by price-updating the weights
laspeyres_index(p2, p0, q0)
#> [1] 1.05
arithmetic_mean(p1 / p0, s0) *
arithmetic_mean(p2 / p1, update_weights(p1 / p0, s0))
#> [1] 1.05
# The mean representation of a Paasche index makes it easy to
# calculate percent-change contributions
harmonic_contributions(p1 / p0, s1)
#> [1] 0.02857143 0.71428571 0.04642857 -0.02500000 0.06666667 -0.01904762
# The ideas are the same for more exotic indexes,
# like the Lloyd-Moulton index
# Let's start by making some functions for the Lloyd-Moulton index
# when the elasticity of substitution is -1 (an output index)
lloyd_moulton <- lm_index(-1)
quadratic_mean <- generalized_mean(2)
quadratic_update <- factor_weights(2)
quadratic_contributions <- contributions(2)
# This index can be calculated as a mean of price relatives
lloyd_moulton(p1, p0, q0)
#> [1] 1.592692
quadratic_mean(p1 / p0, s0)
#> [1] 1.592692
# Chained over time
lloyd_moulton(p2, p0, q0)
#> [1] 1.136515
quadratic_mean(p1 / p0, s0) *
quadratic_mean(p2 / p1, quadratic_update(p1 / p0, s0))
#> [1] 1.136515
# And decomposed to get the contributions of each relative
quadratic_contributions(p1 / p0, s0)
#> [1] 0.03110568 0.51154526 0.04832926 -0.03830484 0.06666667 -0.02665039
```

There are a number of R packages on the CRAN that implement the standard
index-number formulas (e.g., **IndexNumber**, **productivity**,
**IndexNumR**, **micEconIndex**, **PriceIndices**). While there is
support for a large number of index-number formulas out-of-the box in
this package, the focus is on the tools to easily make and work with any
type of generalized-mean price index. Consequently, compared to existing
packages, this package is suitable for building custom price/quantity
indexes, calculating indexes with sample data, decomposing indexes, and
learning about or researching different types of index-number formulas.

Balk, B. M. (2008). *Price and Quantity Index Numbers*. Cambridge
University Press.

IMF, ILO, Eurostat, UNECE, OECD, and World Bank. (2020). *Consumer Price
Index Manual: Concepts and Methods*. International Monetary Fund.

von der Lippe, P. (2007). *Index Theory and Price Statistics*. Peter
Lang.

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