**author: Jacek Białek, University of Lodz, Statistics
Poland**

Goals of PriceIndices are as follows: a) data processing before price index calculations; b) bilateral and multilateral price index calculations; c) extending multilateral price indices. You can download the package documentation from here. Too read more about the package please see (and cite :)) papers:

Białek, J. (2021). PriceIndices – a New R Package for Bilateral and Multilateral Price Index Calculations, Statistika – Statistics and Economy Journal, Vol. 2/2021, 122-141, Czech Statistical Office, Praga.

Białek, J. (2022). Scanner data processing in a newest version of the PriceIndices package, Statistical Journal of the IAOS, 38 (4), 1369-1397, DOI: 10.3233/SJI-220963.

You can install the released version of **PriceIndices**
from CRAN with:

`install.packages("PriceIndices")`

You can install the development version of
**PriceIndices** from GitHub with:

```
library("remotes")
::install_github("JacekBialek/PriceIndices") remotes
```

- Data sets included in the package and generating artificial scanner data sets
- Functions for data processing
- Functions providing dataset characteristics
- Functions for bilateral unweighted price index calculations
- Functions for bilateral weighted price index calculations
- Functions for chain price index calculations
- Functions for multilateral price index calculations
- Functions for extending multilateral price indices by using splicing methods
- Functions for extending multilateral price indices by using the FBEW method
- Functions for extending multilateral price indices by using the FBMW method
- General functions for price index calculations
- Functions for comparisons of price indices
- Functions for price and quantity indicator calculations

**This package includes seven data sets: artificial and
real.**

*1) dataAGGR*

The first one, **dataAGGR**, can be used to demonstrate
the **data_aggregating** function. This is a collection of
artificial scanner data on milk products sold in three different months
and it contains the following columns: **time** - dates of
transactions (Year-Month-Day: 4 different dates);
**prices** - prices of sold products (PLN);
**quantities** - quantities of sold products (liters);
*prodID* - unique product codes (3 different prodIDs);
**retID** - unique codes identifying outlets/retailer sale
points (4 different retIDs); **description** - descriptions
of sold products (two subgroups: goat milk, powdered milk).

*2) dataMATCH*

The second one, **dataMATCH**, can be used to
demonstrate the **data_matching** function and it will be
described in the next part of the guidelines. Generally, this artificial
data set contains the following columns: **time** - dates
of transactions (Year-Month-Day); **prices** - prices of
sold products; **quantities** - quantities of sold
products; **codeIN** - internal product codes from the
retailer; **codeOUT** - external product codes, e.g. GTIN
or SKU in the real case; **description** - descriptions of
sold products, eg. ‘product A’, ‘product B’, etc.

*3) dataCOICOP*

The third one, **dataCOICOP**, is a ollection of real
scanner data on the sale of milk products sold in a period: Dec, 2020 -
Feb, 2022. It is a data frame with 10 columns and 139600 rows. The used
variables are as follows: **time** - dates of transactions
(Year-Month-Day); **prices** - prices of sold products
(PLN); **quantities** - quantities of sold products;
**description** - descriptions of sold products (original:
in Polish); **codeID** - retailer product codes;
**retID** - IDs of retailer outlets;
**grammage** - product grammages; **unit** -
sales units, e.g. ‘kg’, ‘ml’, etc.; **category** - product
categories (in English) corresponding to COICOP 6 levels;
**coicop6** - identifiers of local COICOP 6 groups (6
levels). Please note that this data set can serve as a training or
testing set in product classification using machine learning methods
(see the functions: **model_classification** and
**data_classifying**).

*4) milk*

This data set, **milk**, is a collection of scaner data
on the sale of milk in one of Polish supermarkets in the period from
December 2018 to August 2020. It is a data frame with 6 columns and 4386
rows. The used variables are as follows: **time** - dates
of transactions (Year-Month-Day); **prices** - prices of
sold products (PLN); **quantities** - quantities of sold
products (liters); **prodID** - unique product codes
obtained after product matching (data set contains 68 different
prodIDs); **retID** - unique codes identifying
outlets/retailer sale points (data set contains 5 different retIDs);
**description** - descriptions of sold milk products (data
set contains 6 different product descriptions corresponding to
*subgroups* of the milk group).

*5) coffee*

This data set, **coffee**, is a collection of scanner
data on the sale of coffee in one of Polish supermarkets in the period
from December 2017 to October 2020. It is a data frame with 6 columns
and 42561 rows. The used variables are as follows: **time**
- dates of transactions (Year-Month-Day); **prices** -
prices of sold products (PLN); **quantities** - quantities
of sold products (kg); **prodID** - unique product codes
obtained after product matching (data set contains 79 different
prodIDs); **retID** - unique codes identifying
outlets/retailer sale points (data set contains 20 different retIDs);
**description** - descriptions of sold coffee products
(data set contains 3 different product descriptions corresponding to
*subgroups* of the coffee group).

*6) sugar*

This data set, **sugar**, is a collection of scanner
data on the sale of coffee in one of Polish supermarkets in the period
from December 2017 to October 2020. It is a data frame with 6 columns
and 7666 rows. The used variables are as follows: **time**
- dates of transactions (Year-Month-Day); **prices** -
prices of sold products (PLN); **quantities** - quantities
of sold products (kg); **prodID** - unique product codes
obtained after product matching (data set contains 11 different
prodIDs); **retID** - unique codes identifying
outlets/retailer sale points (data set contains 20 different retIDs);
**description** - descriptions of sold sugar products (data
set contains 3 different product descriptions corresponding to
*subgroups* of the sugar group).

*7) dataU*

This data set, **dataU**, is a collection of artificial
scanner data on 6 products sold in Dec, 2018. Product descriptions
contain the information about their grammage and unit. It is a data
frame with 5 columns and 6 rows. The used variables are as follows:
**time** - dates of transactions (Year-Month-Day);
**prices** - prices of sold products (PLN);
**quantities** - quantities of sold products (item);
**prodID** - unique product codes;
**description** - descriptions of sold products (data set
contains 6 different product descriptions).

The set **milk** represents a typical data frame used in
the package for most calculations and is organized as follows:

```
library(PriceIndices)
head(milk)
#> time prices quantities prodID retID description
#> 1 2018-12-01 8.78 9.0 14215 2210 powdered milk
#> 2 2019-01-01 8.78 13.5 14215 2210 powdered milk
#> 3 2019-02-01 8.78 0.5 14215 1311 powdered milk
#> 4 2019-02-01 8.78 8.0 14215 2210 powdered milk
#> 5 2019-03-01 8.78 0.5 14215 1311 powdered milk
#> 6 2019-03-01 8.78 1.5 14215 2210 powdered milk
```

Available subgroups of sold milk are

```
unique(milk$description)
#> [1] "powdered milk" "low-fat milk pasteurized"
#> [3] "low-fat milk UHT" "full-fat milk pasteurized"
#> [5] "full-fat milk UHT" "goat milk"
```

**Generating artificial scanner data sets in the
package**

The package includes the **generate** function which
provides an artificial scanner data sets where prices and quantities are
lognormally distributed. The characteristics for these lognormal
distributions are set by **pmi**, **sigma**,
**qmi** and **qsigma** parameters. This
function works for the fixed number of products and outlets (see
**n** and **r** parameters). The generated
data set is ready for further price index calculations. For
instance:

```
<-generate(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02),
datasetqmi=c(3,4,4),qsigma=c(0.1,0.1,0.15),
start="2020-01")
head(dataset)
#> time prices quantities prodID retID
#> 1 2020-01-01 2.78 23 1 1
#> 2 2020-01-01 2.75 19 2 1
#> 3 2020-01-01 2.88 21 3 1
#> 4 2020-01-01 2.66 19 4 1
#> 5 2020-01-01 2.58 20 5 1
#> 6 2020-01-01 2.61 18 6 1
```

From the other hand you can use **tindex** function to
obtain the theoretical value of the unweighted price index for
lognormally distributed prices (the month defined by
**start** parameter plays a role of the fixed base period).
The characteristics for these lognormal distributions are set by
**pmi** and **sigma** parameters. The
**ratio** parameter is a logical parameter indicating how
we define the theoretical unweighted price index. If it is set to TRUE
then the resulting value is a ratio of expected price values from
compared months; otherwise the resulting value is the expected value of
the ratio of prices from compared months.The function provides a data
frame consisting of dates and corresponding expected values of the
theoretical unweighted price index. For example:

```
tindex(pmi=c(1.02,1.03,1.04),psigma=c(0.05,0.09,0.02),start="2020-01",ratio=FALSE)
#> date tindex
#> 1 2020-01 1.000000
#> 2 2020-02 1.012882
#> 3 2020-03 1.019131
```

The User may also generate an artificial scanner dataset where prices
are lognormally distributed and quantities are calculated under the
assumption that consumers have CES (Constant Elasticity of Substitution)
preferences and their spending on all products is fixed (see the
**generate_CES** function). Please watch the following
example:

```
#Generating an artificial dataset (the elasticity of substitution is 1.25)
<-generate_CES(pmi=c(1.02,1.03),psigma=c(0.04,0.03),
dfelasticity=1.25,start="2020-01",n=100,days=TRUE)
head(df)
#> time prices quantities prodID retID
#> 1 2020-01-09 2.98 1.447739 1 1
#> 2 2020-01-25 2.89 4.001540 2 1
#> 3 2020-01-12 2.90 2.068384 3 1
#> 4 2020-01-14 2.72 1.437393 4 1
#> 5 2020-01-04 2.75 5.446941 5 1
#> 6 2020-01-28 2.80 7.435235 6 1
```

Now, we can verify the value of elasticity of substitution using this generated dataset:

```
#Verifying the elasticity of substitution
elasticity(df, start="2020-01",end="2020-02")
#> [1] 1.25
```

**data_preparing**

This function returns a prepared data frame based on the user’s data
set (you can check if your data set it is suitable for further price
index calculation by using **data_check** function). The
resulting data frame is ready for further data processing (such as data
selecting, matching or filtering) and it is also ready for price index
calculations (if only it contains the required columns). The resulting
data frame is free from missing values, negative and (optionally) zero
prices and quantities. As a result, the column **time** is
set to be **Date** type (in format: ‘Year-Month-01’), while
the columns **prices** and **quantities** are
set to be **numeric**. If the **description**
parameter is set to *TRUE* then the column
**description** is set to be **character**
type (otherwise it is deleted). Please note that the
**milk** set is an already prepared dataset but let us
assume for a moment that we want to make sure that it does not contain
missing values and we do not need the column
**description** for further calculations. For this purpose,
we use the **data_preparing** function as follows:

```
head(data_preparing(milk, time="time",prices="prices",quantities="quantities"))
#> time prices quantities
#> 1 2018-12-01 8.78 9.0
#> 2 2019-01-01 8.78 13.5
#> 3 2019-02-01 8.78 0.5
#> 4 2019-02-01 8.78 8.0
#> 5 2019-03-01 8.78 0.5
#> 6 2019-03-01 8.78 1.5
```

**data_imputing**

This function imputes missing prices (unit values) and (optionally) zero prices by using carry forward/backward prices. The imputation can be done for each outlet separately or for aggragated data (see the outlets parameter). If a missing product has a previous price then that previous price is carried forward until the next real observation. If there is no previous price then the next real observation is found and carried backward. The quantities for imputed prices are set to zeros. The function returns a data frame which is ready for price index calculations, for instance:

```
#Creating a data frame with zero prices (df)
<-dplyr::filter(milk,time>=as.Date("2018-12-01") & time<=as.Date("2019-03-01"))
data<-dplyr::sample_n(data, 100)
sample<-setdiff(data, sample)
rest$prices<-0
sample<-rbind(sample, rest)
df#The Fisher price index calculated for the original data set
fisher(df, "2018-12","2019-03")
#> [1] 0.9406833
#Zero price imputations:
<-data_imputing(df, start="2018-12", end="2019-03",
df2zero_prices=TRUE,
outlets=TRUE)
#The Fisher price index calculated for the data set with imputed prices:
fisher(df2, "2018-12","2019-03")
#> [1] 0.9403425
```

**data_aggregating**

The function aggregates the user’s data frame over time and/or over
outlets. Consequently, we obtain monthly data, where the unit value is
calculated instead of a price for each **prodID** observed
in each month (the time column gets the Date format: “Year-Month-01”).
If paramter **join_outlets** is *TRUE*, then the
function also performs aggregation over outlets (*retIDs*) and
the **retID** column is removed from the data frame. The
main advantage of using this function is the ability to reduce the size
of the data frame and the time needed to calculate the price index. For
instance, let us consider the following data set:

```
dataAGGR#> time prices quantities prodID retID description
#> 1 2018-12-01 10 100 400032 4313 goat milk
#> 2 2018-12-01 15 100 400032 1311 goat milk
#> 3 2018-12-01 20 100 400032 1311 goat milk
#> 4 2020-07-01 20 100 400050 1311 goat milk
#> 5 2020-08-01 30 50 400050 1311 goat milk
#> 6 2020-08-01 40 50 400050 2210 goat milk
#> 7 2018-12-01 15 200 403249 2210 powdered milk
#> 8 2018-12-01 15 200 403249 2210 powdered milk
#> 9 2018-12-01 15 300 403249 2210 powdered milk
```

After aggregating this data set over time and outlets we obtain:

```
data_aggregating(dataAGGR)
#> # A tibble: 4 x 4
#> time prodID prices quantities
#> <date> <int> <dbl> <int>
#> 1 2018-12-01 400032 15 300
#> 2 2018-12-01 403249 15 700
#> 3 2020-07-01 400050 20 100
#> 4 2020-08-01 400050 35 100
```

**data_unit**

The function returns the user’s data frame with two additional
columns: **grammage** and **unit** (both are
character type). The values of these columns are extracted from product
descriptions on the basis of provided **units**. Please
note, that the function takes into consideration a sign of the
multiplication, e.g. if the product description contains: ‘2x50 g’, we
will obtain: **grammage: 100** and **unit: g**
for that product (for **multiplication** set to ‘x’). For
example:

```
data_unit(dataU,units=c("g","ml","kg","l"),multiplication="x")
#> time prices quantities prodID description grammage unit
#> 1 2018-12-01 8.00 200 40033 drink 0,75l 3% corma 0.75 l
#> 2 2018-12-01 5.20 300 12333 sugar 0.5kg 0.5 kg
#> 3 2018-12-01 10.34 100 20345 milk 4x500ml 2000 ml
#> 4 2018-12-01 2.60 500 15700 xyz 3 4.34 xyz 200 g 200 g
#> 5 2018-12-01 12.00 1000 13022 abc 1 item
#> 6 2019-01-01 3.87 250 10011 ABC 2A/350 g mnk 350 g
```

**data_norm**

The function returns the user’s data frame with two transformed
columns: **grammage** and **unit**, and two
rescaled columns: **prices** and
**quantities**. The above-mentioned transformation and
rescaling take into consideration the user **rules**.
Recalculated prices and quantities concern grammage units defined as the
second parameter in the given rule. For instance:

```
# Preparing a data set
<-data_unit(dataU,units=c("g","ml","kg","l"),multiplication="x")
data# Normalization of grammage units
data_norm(data, rules=list(c("ml","l",1000),c("g","kg",1000)))
#> time prices quantities prodID description grammage unit
#> 1 2018-12-01 5.17000 200.0 20345 milk 4x500ml 2 l
#> 2 2018-12-01 10.66667 150.0 40033 drink 0,75l 3% corma 0.75 l
#> 3 2018-12-01 13.00000 100.0 15700 xyz 3 4.34 xyz 200 g 0.2 kg
#> 4 2019-01-01 11.05714 87.5 10011 ABC 2A/350 g mnk 0.35 kg
#> 5 2018-12-01 10.40000 150.0 12333 sugar 0.5kg 0.5 kg
#> 6 2018-12-01 12.00000 1000.0 13022 abc 1 item
```

**data_selecting**

The function returns a subset of the user’s data set obtained by
selection based on keywords and phrases defined by parameters:
**include**, **must** and
**exclude** (an additional column **coicop**
is optional). Providing values of these parameters, please remember that
the procedure distinguishes between uppercase and lowercase letters only
when **sensitivity** is set to *TRUE*.

For instance, please use

```
<-data_selecting(milk, include=c("milk"), must=c("UHT"))
subgroup1head(subgroup1)
#> time prices quantities prodID retID description
#> 1 2018-12-01 2.97 78 17034 1311 low-fat milk uht
#> 2 2018-12-01 2.97 167 17034 2210 low-fat milk uht
#> 3 2018-12-01 2.97 119 17034 6610 low-fat milk uht
#> 4 2018-12-01 2.97 32 17034 7611 low-fat milk uht
#> 5 2018-12-01 2.97 54 17034 8910 low-fat milk uht
#> 6 2019-01-01 2.95 71 17034 1311 low-fat milk uht
```

to obtain the subset of **milk** limited to *UHT*
category:

```
unique(subgroup1$description)
#> [1] "low-fat milk uht" "full-fat milk uht"
```

You can use

```
<-data_selecting(milk, must=c("milk"), exclude=c("past","goat"))
subgroup2head(subgroup2)
#> time prices quantities prodID retID description
#> 1 2018-12-01 8.78 9.0 14215 2210 powdered milk
#> 2 2019-01-01 8.78 13.5 14215 2210 powdered milk
#> 3 2019-02-01 8.78 0.5 14215 1311 powdered milk
#> 4 2019-02-01 8.78 8.0 14215 2210 powdered milk
#> 5 2019-03-01 8.78 0.5 14215 1311 powdered milk
#> 6 2019-03-01 8.78 1.5 14215 2210 powdered milk
```

to obtain the subset of **milk** with products which are
not *pasteurized* and which are not **goat**:

```
unique(subgroup2$description)
#> [1] "powdered milk" "low-fat milk uht" "full-fat milk uht"
```

**data_classifying**

This function predicts product COICOP levels (or any other defined
product levels) using the selected machine learning model (see the
**model** parameter). It provides the indicated data set
with an additional column, i.e. *coicop_predicted*. The selected
model must be built previously (see the
**model_classification** function) and after the training
process it can be saved on your disk (see the
**save_model** function) and then loaded at any time (see
the **load_model** function). Please note that the machine
learning process is based on the XGBoost algorithm (from the XGBoost
package) which is an implementation of gradient boosted decision trees
designed for speed and performance. For example, let us build a machine
learning model

```
=list(eta=c(0.01,0.02,0.05),subsample=c(0.5,0.8))
my.grid<-dplyr::filter(dataCOICOP,dataCOICOP$time<=as.Date("2021-10-01"))
data_train<-dplyr::filter(dataCOICOP,dataCOICOP$time==as.Date("2021-11-01"))
data_test<-model_classification(data_train,
ML
data_test,coicop="coicop6",
grid=my.grid,
indicators=c("description","codeIN","grammage"),
key_words=c("uht"),
rounds=60)
```

We can watch the results of the whole training process:

`$figure_training ML`

or we can observe the importance of the used indicators:

`$figure_importance ML`

Now, let us save the model on the disk. After saving the model we can load it and use at any time:

```
#Setting a temporary directory as a working directory
<-tempdir()
wdsetwd(wd)
#Saving and loading the model
save_model(ML, dir="My_model")
<-load_model("My_model")
ML_fromPC#Prediction
<-data_classifying(ML_fromPC, data_test)
data_predictedhead(data_predicted)
#> time prices quantities description codeIN
#> 1 2021-11-01 3.03 379 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> 2 2021-11-01 3.03 856 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> 3 2021-11-01 3.03 369 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> 4 2021-11-01 3.03 617 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> 5 2021-11-01 3.03 613 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> 6 2021-11-01 3.03 261 g/wydojone mleko bez laktozyuht 3,2%1l 60001
#> retID grammage unit category coicop6 coicop_predicted
#> 1 2 1 l UHT whole milk 11411_1 11411_1
#> 2 3 1 l UHT whole milk 11411_1 11411_1
#> 3 4 1 l UHT whole milk 11411_1 11411_1
#> 4 5 1 l UHT whole milk 11411_1 11411_1
#> 5 6 1 l UHT whole milk 11411_1 11411_1
#> 6 7 1 l UHT whole milk 11411_1 11411_1
```

**data_matching**

If you have a dataset with information about products sold but they
are not matched you can use the **data_matching** function.
In an optimal situation, your data frame contains the
**codeIN**, **codeOUT** and
**description** columns (see documentation), which in
practice will contain *retailer codes*, *GTIN* or
*SKU* codes and *product labels*, respectively. The
**data_matching** function returns a data set defined in
the first parameter (*data*) with an additional column
(*prodID*). Two products are treated as being matched if they
have the same prodID value. The procedure of generating the
above-mentioned additional column depends on the set of chosen columns
for matching (see documentation for details). For instance, let us
suppose you want to obtain matched products from the following,
artificial data set:

```
head(dataMATCH)
#> time prices quantities codeIN codeOUT retID description
#> 1 2018-12-01 9.416371 309 1 1 1 product A
#> 2 2019-01-01 9.881875 325 1 5 1 product A
#> 3 2019-02-01 12.611826 327 1 1 1 product A
#> 4 2018-12-01 9.598252 309 3 2 1 product A
#> 5 2019-01-01 9.684900 325 3 2 1 product A
#> 6 2019-02-01 9.358420 327 3 2 1 product A
```

Let us assume that products with two identical codes
(**codeIN** and **codeOUT**) or one of the
codes identical and an identical description are automatically matched.
Products are also matched if they have one of the codes identical and
the *Jaro-Winkler similarity* of their descriptions is bigger
than the fixed **precision** value (see documentation -
*Case 1*). Let us also suppose that you want to match all
products sold in the interval: December 2018 - February 2019. If you use
the **data_matching** function (as below), an additional
column (**prodID**) will be added to your data frame:

```
<-data_matching(dataMATCH, start="2018-12",end="2019-02", codeIN=TRUE, codeOUT=TRUE, precision=.98, interval=TRUE)
data1head(data1)
#> time prices quantities codeIN codeOUT retID description prodID
#> 1 2018-12-01 9.416371 309 1 1 1 product A 4
#> 2 2019-01-01 9.881875 325 1 5 1 product A 4
#> 3 2019-02-01 12.611826 327 1 1 1 product A 4
#> 4 2018-12-01 9.598252 309 3 2 1 product A 8
#> 5 2019-01-01 9.684900 325 3 2 1 product A 8
#> 6 2019-02-01 9.358420 327 3 2 1 product A 8
```

Let us now suppose you do not want to consider
**codeIN** while matching and that products with an
identical **description** are to be matched too:

```
<-data_matching(dataMATCH, start="2018-12",end="2019-02",
data2codeIN=FALSE, onlydescription=TRUE, interval=TRUE)
head(data2)
#> time prices quantities codeIN codeOUT retID description prodID
#> 1 2018-12-01 9.416371 309 1 1 1 product A 7
#> 2 2019-01-01 9.881875 325 1 5 1 product A 7
#> 3 2019-02-01 12.611826 327 1 1 1 product A 7
#> 4 2018-12-01 9.598252 309 3 2 1 product A 7
#> 5 2019-01-01 9.684900 325 3 2 1 product A 7
#> 6 2019-02-01 9.358420 327 3 2 1 product A 7
```

Now, having a **prodID** column, your datasets are ready
for further price index calculations, e.g.:

```
fisher(data1, start="2018-12", end="2019-02")
#> [1] 1.018419
jevons(data2, start="2018-12", end="2019-02")
#> [1] 1.074934
```

**data_filtering**

This function returns a filtered data set, i.e. a reduced user’s data
frame with the same columns and rows limited by a criterion defined by
the **filters** parameter (see documentation). If the set
of filters is empty then the function returns the original data frame
(defined by the **data** parameter). On the other hand, if
all filters are chosen, i.e. *filters=c(extremeprices, dumpprices,
lowsales)*, then these filters work independently and a summary
result is returned. Please note that both variants of the
*extremeprices* filter can be chosen at the same time,
i.e. *plimits* and *pquantiles*, and they work also
independently. For example, let us assume we consider three filters:
**filter1** is to reject 1% of the lowest and 1% of the
highest price changes comparing March 2019 to December 2018,
**filter2** is to reject products with the price ratio
being less than 0.5 or bigger than 2 in the same time,
**filter3** rejects the same products as
**filter2** rejects and also products with relatively
*low sale* in compared months, **filter4** rejects
products with the price ratio being less than 0.9 and with the
expenditure ratio being less than 0.8 in the same time.

```
<-data_filtering(milk,start="2018-12",end="2019-03",
filter1filters=c("extremeprices"),pquantiles=c(0.01,0.99))
<-data_filtering(milk,start="2018-12",end="2019-03",
filter2filters=c("extremeprices"),plimits=c(0.5,2))
<-data_filtering(milk,start="2018-12",end="2019-03",
filter3filters=c("extremeprices","lowsales"),plimits=c(0.5,2))
<-data_filtering(milk,start="2018-12",end="2019-03",
filter4filters=c("dumpprices"),dplimits=c(0.9,0.8))
```

These three filters differ from each other with regard to the data reduction level:

```
<-data_filtering(milk,start="2018-12",end="2019-03",filters=c())
data_without_filtersnrow(data_without_filters)
#> [1] 413
nrow(filter1)
#> [1] 378
nrow(filter2)
#> [1] 381
nrow(filter3)
#> [1] 170
nrow(filter4)
#> [1] 374
```

You can also use **data_filtering** for each pair of
subsequent months from the considered time interval under the condition
that this filtering is done for each outlet (**retID**)
separately, e.g.

```
<-data_filtering(milk,start="2018-12",end="2019-03",
filter1Bfilters=c("extremeprices"),pquantiles=c(0.01,0.99),
interval=TRUE, retailers=TRUE)
nrow(filter1B)
#> [1] 773
```

**available**

The function returns all values from the indicated column (defined by
the **type** parameter) which occur at least once in one of
compared periods or in a given time interval. Possible values of the
**type** parameter are: **retID**,
**prodID**, **codeIN**,
**codeOUT** or **description** (see
documentation). If the **interval** parameter is set to
FALSE, then the function compares only periods defined by
**period1** and **period2**. Otherwise the
whole time period between period1 and period2 is considered. For
example:

```
available(milk, period1="2018-12", period2="2019-12", type="retID",interval=TRUE)
#> [1] 2210 1311 6610 7611 8910
```

**matched**

The function returns all values from the indicated column (defined by
the **type** parameter) which occur simultaneously in the
compared periods or in a given time interval.Possible values of the
**type** parameter are: **retID**,
**prodID**, **codeIN**,
**codeOUT** or **description** (see
documentation). If the **interval** parameter is set to
FALSE, then the function compares only periods defined by
**period1** and **period2**. Otherwise the
whole time period between period1 and period2 is considered. For
example:

```
matched(milk, period1="2018-12", period2="2019-12", type="prodID",interval=TRUE)
#> [1] 14216 15404 17034 34540 60010 70397 74431 82827 82830 82919
#> [11] 94256 400032 400033 400189 400194 400195 400196 401347 401350 402263
#> [21] 402264 402293 402569 402570 402601 402602 402609 403249 404004 404005
#> [31] 405419 405420 406223 406224 406245 406246 406247 407219 407220 407669
#> [41] 407670 407709 407859 407860 400099
```

**matched_index**

The function returns a ratio of values from the indicated column that
occur simultaneously in the compared periods or in a given time interval
to all available values from the above-mentioned column (defined by the
**type** parameter) at the same time. Possible values of
the **type** parameter are: **retID**,
**prodID**, **codeIN**,
**codeOUT** or **description** (see
documentation). If the **interval** parameter is set to
FALSE, then the function compares only periods defined by period1 and
period2. Otherwise the whole time period between period1 and period2 is
considered. The returned value is from 0 to 1. For example:

```
matched_index(milk, period1="2018-12", period2="2019-12", type="prodID",interval=TRUE)
#> [1] 0.7258065
```

**matched_fig**

The function returns a **data frame** or a
**figure** presenting the **matched_index**
function calculated for the column defined by the **type**
parameter and for each month from the considered time interval. The
interval is set by the **start** and **end**
parameters. The returned object (data frame or figure) depends on the
value of the **figure** parameter. Examples:

`matched_fig(milk, start="2018-12", end="2019-12", type="prodID")`

```
matched_fig(milk, start="2018-12", end="2019-04", type="prodID", figure=FALSE)
#> date fraction
#> 1 2018-12 1.0000000
#> 2 2019-01 0.9629630
#> 3 2019-02 0.9444444
#> 4 2019-03 0.9074074
#> 5 2019-04 0.8727273
```

**products**

This function detects and summarises available, matched, new and
disappearing products on the basis of their prodIDs. It compares
products from the base period (**start**) with products
from the current period (**end**). It returns a list
containing the following objects: details with prodIDs of available,
matched, new and disappearing products, statistics with basic statistics
for them and figure with a pie chart describing a contribution of
matched, new and disappearing products in a set of available products.
Please see the following example:

```
<-products(milk, "2018-12","2019-12")
list$statistics
list#> products volume shares
#> 1 available 61 100.00
#> 2 matched 47 77.05
#> 3 new 8 13.11
#> 4 disappearing 6 9.84
```

`$figure list`

**products_fig**

This function returns a figure with plots of volume (or
contributions) of available, matched, new as well as disappearing
products. The User may control which groups of products are to be taken
into consideration. Available options are **available**,
**matched**, **new** and
**disappearing**. Please follow the example:

```
products_fig(milk, "2018-12","2019-12",
fixed_base=TRUE, contributions=FALSE,
show=c("new","disappearing","matched","available"))
```

**prices**

The function returns prices (unit value) of products with a given ID
(**prodID** column) and being sold in the time period
indicated by the **period** parameter. The
**set** parameter means a set of unique product IDs to be
used for determining prices of sold products. If the set is empty the
function returns prices of all products being available in the
**period**. Please note that the function returns the price
values for sorted prodIDs and in the absence of a given prodID in the
data set, the function returns nothing (it does not return zero).To get
prices (unit values) of all available milk products sold in July, 2019,
please use:

```
prices(milk, period="2019-06")
#> [1] 8.700000 8.669455 1.890000 2.950000 1.990000 2.990000 2.834464
#> [8] 4.702051 2.163273 2.236250 2.810000 2.860000 2.400000 2.588644
#> [15] 3.790911 7.980000 64.057143 7.966336 18.972121 12.622225 9.914052
#> [22] 7.102823 3.180000 2.527874 1.810000 1.650548 2.790000 2.490000
#> [29] 2.590000 7.970131 9.901111 15.266667 19.502286 2.231947 2.674401
#> [36] 2.371819 2.490000 6.029412 6.441176 2.090000 1.990000 1.890000
#> [43] 1.450000 2.680000 2.584184 2.683688 2.390000 3.266000 2.813238
```

**quantities**

The function returns quantities of products with a given ID
(**prodID** column) and being sold in the time period
indicated by the **period** parameter. The
**set** parameter means a set of unique product IDs to be
used for determining prices of sold products. If the set is empty the
function returns quantities of all products being available in the
**period**. Please note that the function returns the
quantity values for sorted prodIDs and in the absence of a given prodID
in the data set, the function returns nothing (it does not return zero).
To get a data frame containing quantities of milk products with prodIDs:
400032, 71772 and 82919, and sold in July, 2019, please use:

```
quantities(milk, period="2019-06", set=c(400032, 71772, 82919), ID=TRUE)
#> # A tibble: 3 x 2
#> by q
#> <int> <dbl>
#> 1 71772 117
#> 2 82919 102
#> 3 400032 114.
```

**sales**

The function returns values of sales of products with a given ID
(**prodID** column) and being sold in the time period
indicated by **period** parameter. The **set**
parameter means a set of unique product IDs to be used for determining
prices of sold products. If the set is empty the function returns values
of sales of all products being available in the **period**
(see also **expenditures** function which returns the
expenditure values for sorted prodIDs). To get values of sales of milk
products with prodIDs: 400032, 71772 and 82919, and sold in July, 2019,
please use:

```
sales(milk, period="2019-06", set=c(400032, 71772, 82919))
#> [1] 913.71 550.14 244.80
```

**sales_groups**

The function returns **values of sales** of products
from one or more **datasets** or the corresponding
**barplot** for these sales (if **barplot** is
set to TRUE). Alternatively, it calculates the **sale
shares** (if the **shares** parameter is set to
TRUE). Please see also the **sales_groups2** function. As
an example, let us create 3 subgroups of **milk** products
and let us find out their sale shares for the time interval: April, 2019
- July, 2019. We can obtain precise values for the given
**period**:

```
<-unique(milk$description)
ctg<-c(ctg[1],ctg[2],ctg[3])
categories<-dplyr::filter(milk, milk$description==categories[1])
milk1<-dplyr::filter(milk, milk$description==categories[2])
milk2<-dplyr::filter(milk, milk$description==categories[3])
milk3sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07")
#> [1] 44400.76 152474.55 101470.76
sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07", shares=TRUE)
#> [1] 0.1488230 0.5110661 0.3401109
```

or a barplot presenting these results:

```
sales_groups(datasets=list(milk1,milk2,milk3),start="2019-04", end="2019-07",
barplot=TRUE, shares=TRUE, names=categories)
```

**pqcor**

The function returns **Pearson’s correlation
coefficient** for price and quantity of products with given IDs
(defined by the **set** parameter) and sold in the
**period**. If the **set** is empty, the
function works for all products being available in the
**period**. The **figure** parameter indicates
whether the function returns a figure with a correlation coefficient
(TRUE) or just a correlation coefficient (FALSE). For instance:

```
pqcor(milk, period="2019-05")
#> [1] -0.2047
pqcor(milk, period="2019-05",figure=TRUE)
```

**pqcor_fig**

The function returns **Pearson’s correlation
coefficients** between price and quantity of products with given
IDs (defined by the **set** parameter) and sold in the time
interval defined by the **start** and **end**
parameters. If the **set** is empty the function works for
all available products. Correlation coefficients are calculated for each
month separately. Results are presented in tabular or graphical form
depending on the **figure** parameter. Both cases are
presented below:

```
pqcor_fig(milk, start="2018-12", end="2019-06", figure=FALSE)
#> date correlation
#> 1 2018-12 -0.1835
#> 2 2019-01 -0.1786
#> 3 2019-02 -0.1805
#> 4 2019-03 -0.1956
#> 5 2019-04 -0.1972
#> 6 2019-05 -0.2047
#> 7 2019-06 -0.2037
pqcor_fig(milk, start="2018-12", end="2019-06")
```

**dissimilarity**

This function returns a value of the relative price (dSP) and/or
quantity (dSQ) dissimilarity measure. In a special case, when the
**type** parameter is set to **pq**, the
function provides the value of dSPQ measure (relative price and quantity
dissimilarity measure calculated as **min(dSP,dSQ)**. For
instance:

```
dissimilarity(milk, period1="2018-12",period2="2019-12",type="pq")
#> [1] 0.00004175192
```

**dissimilarity_fig**

This function presents values of the relative price and/or quantity
dissimilarity measure over time. The user can choose a benchmark period
(defined by **benchmark**) and the type of dissimilarity
measure is to be calculated (defined by **type**). The
obtained results of dissimilarities over time can be presented in a
dataframe form or via a figure (the default value of
**figure** is TRUE which results a figure). For
instance:

`dissimilarity_fig(milk, start="2018-12",end="2019-12",type="pq",benchmark="start")`

**elasticity**

This function returns a value of the elasticity of substitution. If
the **method** parameter is set to **lm** (it
is a default value), the procedure of estimation solves the equation:
LM(sigma)-CW(sigma)=0 numerically, where LM denotes the Lloyd-Moulton
price index, the CW denotes a current weight counterpart of the
Lloyd-Moulton price index, and sigma is the elasticity of substitution
parameter, which is estimated. If the **method** parameter
is set to **f**, the Fisher price index formula is used
instead of the CW price index. If the **method** parameter
is set to **t**, the Tornqvist price index formula is used
instead of the CW price index. If the **method** parameter
is set to **w**, the Walsh price index formula is used
instead of the CW price index. If the **method** parameter
is set to **sv**, the Sato-Vartia price index formula is
used instead of the CW price index.The procedure continues until the
absolute value of this difference is greater than the value of the
‘precision’ parameter. For example:

```
elasticity(coffee, start = "2018-12", end = "2019-01")
#> [1] 4.241791
```

**elasticity_fig**

The function provides a data frame or a figure presenting
elasticities of substitution calculated for time interval (see the
**figure** parameter). The elasticities of substitution can
be calculated for subsequent months or for a fixed base month (see the
**start** parameter) and rest of months from the given time
interval (it depends on the **fixedbase** parameter). The
presented function is based on the **elasticity** function.
For instance, to get elasticities of substitution calculated for milk
products for subsequent months we run:

```
elasticity_fig (milk,start="2018-12",end="2019-04",figure=TRUE,
method=c("lm","f","sv"),names=c("LM","Fisher", "SV"))
```

This package includes 6 functions for calculating the following bilateral unweighted price indices:

Price Index | Function |
---|---|

BMW (2007) | bmw |

Carli (1804) | carli |

CSWD (1980,1992) | cswd |

Dutot (1738) | dutot |

Jevons (1865) | jevons |

Harmonic | harmonic |

Each of these functions returns a value (or vector of values) of the
choosen unweighted bilateral price index depending on the
**interval** parameter. If the interval parameter is set to
TRUE, the function returns a vector of price index values without dates.
To get information about both price index values and corresponding dates
please see general functions: **price_indices** or
**final_index**. None of these functions takes into account
aggregating over outlets or product subgroups (to consider these types
of aggregating please use the **final_index** function.)
Below are examples of calculations for the Jevons index (in the second
case a *fixed base month* is set to December 2018):

```
jevons(milk, start="2018-12", end="2020-01")
#> [1] 1.028223
jevons(milk, start="2018-12", end="2020-01", interval=TRUE)
#> [1] 1.0000000 1.0222661 1.0300191 1.0353857 1.0075504 1.0395393 0.9853148
#> [8] 1.0053100 1.0033727 1.0177604 1.0243906 1.0086291 1.0249373 1.0282234
```

This package includes 30 functions for calculating the following bilateral weighted price indices:

Price Index | Function |
---|---|

AG Mean (2009) | agmean |

Banajree (1977) | banajree |

Bialek (2012,2013) | bialek |

Davies (1924) | davies |

Drobisch (1871) | drobisch |

Fisher (1922) | fisher |

Geary-Khamis (1958,1970) | geary_khamis |

Geo-Laspeyres | geolaspeyres |

Geo-Lowe | geolowe |

Geo-Paasche | geopaasche |

Geo-Young | geoyoung |

Geo-hybrid (2020) | geohybrid |

Hybrid (2020) | hybrid |

Laspeyres (1871) | laspeyres |

Lehr (1885) | lehr |

Lloyd-Moulton (1975,1996) | lloyd_moulton |

Lowe | lowe |

Marshall-Edgeworth (1887) | marshall_edgeworth |

Paasche (1874) | paasche |

Palgrave (1886) | palgrave |

Sato-Vartia (1976) | sato_vartia |

Stuvel (1957) | stuvel |

Tornqvist (1936) | tornqvist |

Vartia (1976) | vartia |

Walsh (1901) | walsh |

Young | young |

Quadratic mean of order r price index | QMp |

Implicit quadratic mean of order r price index | IQMp |

Value Index | value_index |

Unit Value Index | unit_value_index |

and the general quadratic mean of order r quantity index: QMq.

Each of these functions returns a value (or vector of values) of the
choosen weighted bilateral price index depending on the
**interval** parameter. If interval parameter is set to
TRUE, the function returns a vector of price index values without dates.
To get information about both price index values and corresponding dates
please see general functions: **price_indices** or
**final_index**. None of these functions takes into account
aggregating over outlets or product subgroups (to consider these types
of aggregating please use the **final_index** function.)
Below are examples of calculations for the Fisher, the Lloyd-Moulton and
the Lowe indices (in the last case, the *fixed base month* is set
to December 2019 and the *prior* period is December 2018):

```
fisher(milk, start="2018-12", end="2020-01")
#> [1] 0.9615501
lloyd_moulton(milk, start="2018-12", end="2020-01", sigma=0.9)
#> [1] 0.9835069
lowe(milk, start="2019-12", end="2020-02", base="2018-12", interval=TRUE)
#> [1] 1.0000000 0.9880546 1.0024443
```

This package includes 35 functions for calculating the following chain indices (weighted and unweighted):

Price Index | Function |
---|---|

Chain BMW | chbmw |

Chain Carli | chcarli |

Chain CSWD | chcswd |

Chain Dutot | chdutot |

Chain Jevons | chjevons |

Chain Harmonic | chharmonic |

Chain AG Mean | chagmean |

Chain Banajree | chbanajree |

Chain Bialek | chbialek |

Chain Davies | chdavies |

Chain Drobisch | chdrobisch |

Chain Fisher | chfisher |

Chain Geary-Khamis | chgeary_khamis |

Chain Geo-Laspeyres | chgeolaspeyres |

Chain Geo-Lowe | chgeolowe |

Chain Geo-Paasche | chgeopaasche |

Chain Geo-Young | chgeoyoung |

Chain Geo-hybrid | chgeohybrid |

Chain Hybrid | chhybrid |

Chain Laspeyres | chlaspeyres |

Chain Lehr | chlehr |

Chain Lloyd-Moulton | chlloyd_moulton |

Chain Lowe | chlowe |

Chain Marshall-Edgeworth | chmarshall_edgeworth |

Chain Paasche | chpaasche |

Chain Palgrave | chpalgrave |

Chain Sato-Vartia | chsato_vartia |

Chain Stuvel | chstuvel |

Chain Tornqvist | chtornqvist |

Chain Vartia | chvartia |

Chain Walsh | chwalsh |

Chain Young | chyoung |

Chain quadratic mean of order r price index | chQMp |

Chain implicit quadratic mean of order r price index | chIQMp |

Chain quadratic mean of order r quantity index | chQMq |

Each time, the **interval** parameter has a logical
value indicating whether the function is to compare the research period
defined by **end** to the base period defined by
**start** (then **interval** is set to FALSE
and it is a default value) or all fixed base indices are to be
calculated. In this second case, all months from the time interval
**<start,end>** are considered and
**start** defines the base period
(**interval** is set to TRUE). Here are examples for the
Fisher chain index:

```
chfisher(milk, start="2018-12", end="2020-01")
#> [1] 0.9618094
chfisher(milk, start="2018-12", end="2020-01", interval=TRUE)
#> [1] 1.0000000 1.0021692 1.0004617 0.9862756 0.9944042 0.9915704 0.9898026
#> [8] 0.9876325 0.9981591 0.9968851 0.9786428 0.9771951 0.9874251 0.9618094
```

This package includes 22 functions for calculating multilateral price
indices and one additional and general function (**QU**)
which calculates the quality adjusted unit value index, i.e.:

Price Index | Function |
---|---|

CCDI | ccdi |

GEKS | geks |

WGEKS | wgeks |

GEKS-J | geksj |

GEKS-W | geksw |

GEKS-L | geksl |

WGEKS-L | wgeksl |

GEKS-GL | geksgl |

WGEKS-GL | wgeksgl |

GEKS-AQU | geksaqu |

WGEKS-AQU | wgeksaqu |

GEKS-AQI | geksaqi |

WGEKS-AQI | wgeksaqi |

GEKS-GAQI | geksgaqi |

GEKS-IQM | geksiqm |

GEKS-QM | geksqm |

GEKS-LM | gekslm |

WGEKS-GAQI | wgeksgaqi |

Geary-Khamis | gk |

Quality Adjusted Unit Value | QU |

Time Product Dummy | tpd |

Unweighted Time Product Dummy | utpd |

SPQ | SPQ |

The above-mentioned 21 multilateral formulas (the
**SPQ** index is an exception) consider the time window
defined by the **wstart** and **window**
parameters, where **window** is a length of the time window
(typically multilateral methods are based on a 13-month time window). It
measures the price dynamics by comparing the **end** period
to the **start** period (both **start** and
**end** must be inside the considered time window). To get
information about both price index values and corresponding dates,
please see functions: **price_indices** or
**final_index**. These functions do not take into account
aggregating over outlets or product subgroups (to consider these types
of aggregating please use function: **final_index** ). Here
are examples for the GEKS formula (see documentation):

```
geks(milk, start="2019-01", end="2019-04",window=10)
#> [1] 0.9912305
geksl(milk, wstart="2018-12", start="2019-03", end="2019-05")
#> [1] 1.002251
```

The **QU** function returns a value of the *quality
adjusted unit value index* (QU index) for the given set of
adjustment factors. An additional **v** parameter is a data
frame with adjustment factors for at least all matched
**prodIDs**. It must contain two columns:
**prodID** with unique product IDs and
**value** with corresponding adjustment factors (see
documentation). The following example starts from creating a data frame
which includes sample adjusted factors:

```
<-base::unique(milk$prodID)
prodID<-stats::runif(length(prodID),1,2)
values<-data.frame(prodID,values)
vhead(v)
#> prodID values
#> 1 14215 1.472867
#> 2 14216 1.378476
#> 3 15404 1.099301
#> 4 17034 1.129296
#> 5 34540 1.435951
#> 6 51583 1.680332
```

and the next step is calculating the QU index which compares December 2019 to December 2018:

```
QU(milk, start="2018-12", end="2019-12", v)
#> [1] 0.962823
```

This package includes 21 functions for calculating splice indices:

Price Index | Function |
---|---|

Splice CCDI | ccdi_splcie |

Splice GEKS | geks_splice |

Splice weighted GEKS | wgeks_splice |

Splice GEKS-J | geksj_splice |

Splice GEKS-W | geksw_splice |

Splice GEKS-L | geksl_splice |

Splice weighted GEKS-L | wgeksl_splice |

Splice GEKS-GL | geksgl_splice |

Splice weighted GEKS-GL | wgeksgl_splice |

Splice GEKS-AQU | geksaqu_splice |

Splice weighted GEKS-AQU | wgeksaqu_splice |

Splice GEKS-AQI | geksaqi_splice |

Splice weighted GEKS-AQI | wgeksaqi_splice |

Splice GEKS-GAQI | geksgaqi_splice |

Splice weighted GEKS-GAQI | wgeksgaqi_splice |

Splice GEKS-IQM | geksiqm_splice |

Splice GEKS-QM | geksqm_splice |

Splice GEKS-LM | gekslm_splice |

Splice Geary-Khamis | gk_splice |

Splice Time Product Dummy | tpd_splice |

Splice unweighted Time Product Dummy | utpd_splice |

These functions return a value (or values) of the selected
multilateral price index extended by using window splicing methods
(defined by the **splice** parameter). Available splicing
methods are: **movement splice**, **window
splice**, **half splice**, **mean
splice** and their additional variants: **window splice on
published indices (WISP)**, **half splice on published
indices (HASP)** and **mean splice on published
indices** (see documentation). The first considered time window
is defined by the **start** and **window**
parameters, where **window** is a length of the time window
(typically multilateral methods are based on a 13-month time window).
Functions measure the price dynamics by comparing the
**end** period to the **start** period,
i.e. if the time interval **<start, end>** exceeds
the defined time window then splicing methods are used. If the
**interval** parameter is set to TRUE, then all fixed base
multilateral indices are presented (the fixed base month is defined by
**start**). To get information about both price index
values and corresponding dates, please see functions:
**price_indices** or **final_index**. These
functions do not take into account aggregating over outlets or product
subgroups (to consider these types of aggregating, please use the
**final_index** function). For instance, let us calculate
the **extended Time Product Dummy** index by using the
**half splice method** with a 10-month time window:

```
tpd_splice(milk, start="2018-12", end="2020-02",window=10,splice="half",interval=TRUE)
#> [1] 1.0000000 1.0038893 1.0000284 0.9837053 0.9954196 0.9924919 0.9913655
#> [8] 0.9866847 0.9998615 0.9949000 0.9806788 0.9808493 0.9888003 0.9628623
#> [15] 1.0021956
```

This package includes 21 functions for calculating extensions of multilateral indices by using the Fixed Base Monthly Expanding Window (FBEW) method:

Price Index | Function |
---|---|

FBEW CCDI | ccdi_fbew |

FBEW GEKS | geks_fbew |

FBEW WGEKS | wgeks_fbew |

FBEW GEKS-J | geksj_fbew |

FBEW GEKS-W | geksw_fbew |

FBEW GEKS-L | geksl_fbew |

FBEW WGEKS-L | wgeksl_fbew |

FBEW GEKS-GL | geksgl_fbew |

FBEW WGEKS-GL | wgeksgl_fbew |

FBEW GEKS-AQU | geksaqu_fbew |

FBEW WGEKS-AQU | wgeksaqu_fbew |

FBEW GEKS-AQI | geksaqi_fbew |

FBEW WGEKS-AQI | wgeksaqi_fbew |

FBEW GEKS-GAQI | geksgaqi_fbew |

FBEW WGEKS-GAQI | wgeksgaqi_fbew |

FBEW GEKS-QM | geksqm_fbew |

FBEW GEKS-IQM | geksiqm_fbew |

FBEW GEKS-LM | gekslm_fbew |

FBEW Geary-Khamis | gk_fbew |

FBEW Time Product Dummy | tpd_fbew |

FBEW unweighted Time Product Dummy | utpd_fbew |

These functions return a value (or values) of the selected
multilateral price index extended by using the FBEW method. The FBEW
method uses a time window with a fixed base month every year (December).
The window is enlarged every month with one month in order to include
information from a new month. The full window length (13 months) is
reached in December of each year. These functions measure the price
dynamics between the **end** and **start**
periods. A month of the **start** parameter must be
December (see documentation). If the distance between
**end** and **start** exceeds 13 months, then
internal Decembers play a role of chain-linking months. To get
information about both price index values and corresponding dates please
see functions: **price_indices** or
**final_index**. These functions do not take into account
aggregating over outlets or product subgroups (to consider these types
of aggregating, please use the **final_index** function).
For instance, let us calculate the **extended GEKS** index
by using the FBEW method. Please note that December 2019 is the
chain-linking month, i.e.:

```
geks_fbew(milk, start="2018-12", end="2020-03")
#> [1] 0.9891602
geks_fbew(milk, start="2018-12", end="2019-12")*
geks_fbew(milk, start="2019-12", end="2020-03")
#> [1] 0.9891602
```

This package includes 21 functions for calculating extensions of multilateral indices by using the Fixed Base Moving Window (FBMW) method:

Price Index | Function |
---|---|

FBMW CCDI | ccdi_fbmw |

FBMW GEKS | geks_fbmw |

FBMW WGEKS | wgeks_fbmw |

FBMW GEKS-J | geksj_fbmw |

FBMW GEKS-W | geksw_fbmw |

FBMW GEKS-L | geksl_fbmw |

FBMW WGEKS-L | wgeksl_fbmw |

FBMW GEKS-GL | geksgl_fbmw |

FBMW WGEKS-GL | wgeksgl_fbmw |

FBMW GEKS-AQU | geksaqu_fbmw |

FBMW WGEKS-AQU | wgeksaqu_fbmw |

FBMW GEKS-AQI | geksaqi_fbmw |

FBMW WGEKS-AQI | wgeksaqi_fbmw |

FBMW GEKS-GAQI | geksgaqi_fbmw |

FBMW WGEKS-GAQI | wgeksgaqi_fbmw |

FBMW GEKS-IQM | geksiqm_fbmw |

FBMW GEKS-QM | geksqm_fbmw |

FBMW GEKS-LM | gekslm_fbmw |

FBMW Geary-Khamis | gk_fbmw |

FBMW Time Product Dummy | tpd_fbmw |

FBMW unweighted Time Product Dummy | utpd_fbmw |

These functions return a value (or values) of the selected
multilateral price index extended by using the FBMW method. They measure
the price dynamics between the **end** and
**start** periods and it uses a 13-month time window with a
fixed base month taken as **year(end)-1**. If the distance
between **end** and **start** exceeds 13
months, then internal Decembers play a role of chain-linking months. A
month of the **start** parameter must be December (see
documentation). To get information about both price index values and
corresponding dates, please see functions:
**price_indices** or **final_index**. These
functions do not take into account aggregating over outlets or product
subgroups (to consider these types of aggregating, please use the
**final_index** function). For instance, let us calculate
the **extended CCDI** index by using the FBMW method.
Please note that December 2019 is the chain-linking month, i.e.:

```
ccdi_fbmw(milk, start="2018-12", end="2020-03")
#> [1] 0.9874252
ccdi_fbmw(milk, start="2018-12", end="2019-12")*
ccdi_fbmw(milk, start="2019-12", end="2020-03")
#> [1] 0.9874252
```

This package includes 3 general functions for price index
calculation. The **start** and **end**
parameters indicate the base and the research period respectively. These
function provide value or values (depending on the
**interval** parameter) of the selected price index formula
or formulas. If the **interval** parameter is set to
**TRUE** then it returns a data frame with two columns:
**dates** and **index values**. Function
**price_indices** does not take into account aggregating
over outlets or product subgroups and to consider these types of
aggregating, please use function: **final_index**.

**price_indices**

This function allows us to compare many price index formulas by using
one command. The general character of this function mean that, for
instance, your one command may calculate two CES indices for two
different values of **sigma** parameter (the elasticity of
substitution) or you can select several splice indices and calculate
them by using different window lengths and different splicing method.
You can control names of columns in the resulting data frame by defining
additional parameters: **names**. Please note that this
function is not the most general in the package, i.e. all selected price
indices are calculated for the same data set defined by the
**data** parameter and the aggregation over subgroups or
outlets are not taken into consideration here (to consider it, please
use function: **final_index**).

For instance:

```
price_indices(milk,
start = "2018-12", end = "2019-12",
formula=c("geks","ccdi","hybrid","fisher",
"QMp","young","geksl_fbew"),
window = c(13, 13),
base = c("2019-03", "2019-03"),
r=c(3), interval=TRUE)
#> time geks ccdi hybrid fisher QMp young
#> 1 2018-12 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
#> 2 2019-01 1.0020172 1.0018004 0.9967071 1.0021692 1.0025266 0.9982428
#> 3 2019-02 1.0001330 0.9997978 1.0009266 0.9983528 0.9983839 1.0005565
#> 4 2019-03 0.9839258 0.9840643 0.9737613 0.9868188 0.9866552 0.9766453
#> 5 2019-04 0.9936427 0.9932822 0.9861536 0.9954079 0.9956790 0.9875892
#> 6 2019-05 0.9899234 0.9898612 0.9866800 0.9904548 0.9905572 0.9874894
#> 7 2019-06 0.9889829 0.9888433 0.9808391 0.9906674 0.9908235 0.9827443
#> 8 2019-07 0.9862652 0.9864494 0.9889462 0.9848588 0.9845825 0.9893828
#> 9 2019-08 0.9981114 0.9978518 1.0012679 0.9987586 0.9989635 1.0005086
#> 10 2019-09 0.9952078 0.9951481 0.9985214 0.9959955 0.9962294 0.9976441
#> 11 2019-10 0.9776535 0.9773428 0.9747949 0.9767235 0.9770339 0.9746506
#> 12 2019-11 0.9805743 0.9815496 0.9948243 0.9771107 0.9762389 0.9943300
#> 13 2019-12 0.9876664 0.9876167 0.9952270 0.9868354 0.9868723 0.9939052
#> geksl_fbew
#> 1 1.0000000
#> 2 1.0021692
#> 3 0.9964178
#> 4 0.9856119
#> 5 0.9914299
#> 6 0.9884677
#> 7 0.9873196
#> 8 0.9874639
#> 9 0.9957917
#> 10 0.9951035
#> 11 0.9739414
#> 12 0.9882475
#> 13 0.9844756
```

or

```
price_indices(coffee,
start = "2018-12", end = "2019-12",
formula=c("laspeyres","paasche","fisher"),
interval=FALSE)
#> price_index value
#> 1 laspeyres 1.0167511
#> 2 paasche 0.9863043
#> 3 fisher 1.0014120
```

**final_index**

This general function returns a value or values of the selected final
price index for the selected type of aggregation of partial results. If
the interval parameter is set to TRUE, then it returns a data frame
where its first column indicates dates and the remaining columns show
corresponding values of all selected price index. A final price index
formula can be any index formula which is available in the PriceIdices
packages (bilateral or multilateral). The formula used for aggregating
partial index results is selected by the **aggr** parameter
and the User decides on directions of aggregation (see
**outlets** and **groups** parameters).

**Example**. Let us calculate the final Fisher price
index (with Laspeyres-type aggregation over outlets and product
subgroups) for the data set on **milk**

```
final_index(milk, start = "2018-12", end = "2019-12",
formula = "fisher", groups = TRUE, outlets = TRUE,
aggr = "laspeyres", by = "description",
interval = TRUE)
#> time final_index
#> 1 2018-12 1.0000000
#> 2 2019-01 1.0043285
#> 3 2019-02 0.9994987
#> 4 2019-03 0.9909980
#> 5 2019-04 0.9955766
#> 6 2019-05 0.9922104
#> 7 2019-06 0.9910091
#> 8 2019-07 0.9862940
#> 9 2019-08 0.9981004
#> 10 2019-09 0.9978900
#> 11 2019-10 0.9764887
#> 12 2019-11 0.9837980
#> 13 2019-12 0.9871036
```

This package includes two functions for a simple graphical comparison
of price indices and two functions for calculating distances between
indices. The first one, i.e. **compare_indices_df**, is
based on the syntax of the **price_indices** function and
thus it allows us to compare price indices calculated on the same data
set. The second function, i.e. **compare_indices_list**,
has a general character since its first argument is a list of data
frames which contain results obtained by using the
**price_indices** or **final_index**
functions. The third one, i.e. **compare_distances**,
calculates (average) distances between price indices, i.e. the mean
absolute distance or root mean square distance is calculated. The next
function, **compare_to_target**, allows to compute
distances between indices from the selected index group and the
indicated target price index. The last function,
**compare_indices_jk**, presents a comparison of selected
indices obtained by using the jackknife method.

**compare_indices_df** and
**compare_indices_list**

These functions return a figure with plots of selected price indices,
which are provided as a data frame (**compare_indices_df**)
or a list of data frames (**compare_indices_list**). For
instance, let us compare the Laspeyres and Paasche indices calculated
for the data set on milk:

```
<-price_indices(milk, start = "2018-12", end = "2019-12",
dfformula=c("laspeyres", "fisher"), interval = TRUE)
compare_indices_df(df)
```

Now, let us compare the impact of the aggregating over outlets on the
price index results (e.g. the Laspeyres formula is the assumed
aggregating method). For this purpose, let us calculate the Fisher price
index in two cases: **case1** without the above-mentioned
aggregation and **case2** which considers that aggregation.
We use the **milk** dataset and the yearly time
interval:

```
<-price_indices(milk, start="2018-12",end="2019-12",
case1formula="fisher", interval=TRUE)
<-final_index(milk, start="2018-12", end="2019-12",
case2formula="fisher",
outlets=TRUE,
aggr = "laspeyres",
interval=TRUE)
```

The comparison of obtained results can be made as follows:

```
compare_indices_list(data=list(case1, case2),
names=c("Fisher without aggregation",
"Fisher with aggregation"))
```

**compare_distances**

The function calculates average distances between price indices and
it returns a data frame with these values for each pair of price
indices. The main **data** parameter is a data frame
containing values of indices which are to be compared. The
**measure** parameter specifies what measure should be used
to compare the indexes. Possible parameter values are: “MAD” (Mean
Absolute Distance) or “RMSD” (Root Mean Square Distance). The results
may be presented in percentage points (see the **pp**
parameter) and we can control how many decimal places are to be used in
the presentation of results (see the **prec**
parameter).

For instance, let us compare the Jevons, Dutot and Carli indices
calculated for the **milk** data set and for the time
interval: December 2018 - December 2019. Let us use the MAD measure for
these comparisons:

```
#Creating a data frame with unweighted bilateral index values
<-price_indices(milk,
dfformula=c("jevons","dutot","carli"),
start="2018-12",
end="2019-12",
interval=TRUE)
#Calculating average distances between indices (in p.p)
compare_distances(df)
#> jevons dutot carli
#> jevons 0.000 2.482 2.093
#> dutot 2.482 0.000 4.420
#> carli 2.093 4.420 0.000
```

**compare_to_target**

The function calculates average distances between considered price
indices and the target price index and it returns a data frame with:
average distances on the basis of all values of compared indices
(**distance** column), average semi-distances on the basis
of values of compared indices which overestimate the target index values
(**distance_upper** column) and average semi-distances on
the basis of values of compared indices which underestimate the target
index values (**distance_lower** column).

For instance, let us compare the Jevons, Laspeyres, Paasche and Walsh
price indices (calculated for the **milk** data set and for
the time interval: December 2018 - December 2019) with the target Fisher
price index:

```
#Creating a data frame with example bilateral indices
<-price_indices(milk,
dfformula=c("jevons","laspeyres","paasche","walsh"),
start="2018-12",end="2019-12",interval=TRUE)
#Calculating the target Fisher price index
<-fisher(milk,start="2018-12",end="2019-12",interval=TRUE)
target_index#Calculating average distances between considered indices and the Fisher index (in p.p)
compare_to_target(df,target=target_index)
#> index distance distance_lower distance_upper
#> 1 jevons 2.759 0.045 2.714
#> 2 laspeyres 1.429 0.000 1.429
#> 3 paasche 1.403 1.403 0.000
#> 4 walsh 0.174 0.113 0.061
```

**compare_indices_jk**

This function presents a comparison of selected indices obtained by
using the jackknife method. In particular, it returns a list with four
elements: **iterations**, which is a data frame with basic
characteristics of the calculated iteration index values (means,
standard deviations, coefficients of variation and results for all
sample), **pseudovalues**, which is a data frame with basic
characteristics of the calculated index pseudovalues obtained in the
jackknife procedure (i.e. the jackknife estimators and their standard
deviations and coefficients of variation),
**figure_iterations** which presents a box-plot for the
calculated iteration index values, and
**figure_pseudovalues** which presents a box-plot for the
calculated index pseudovalues obtained in the jackknife procedure.
Please follow the example, in which the Jevons, Fisher and GEKS indices
are compared by using the jackknife method:

```
#creating a list with jackknife results
<-compare_indices_jk(milk,
comparisonformula=c("jevons","fisher","geks"),
start="2018-12",
end="2019-12",
window=c(13),
names=c("Jevons","Fisher","GEKS"),
by="retID",
title_iterations="Box-plots for iteration values",
title_pseudovalues="Box-plots for pseudovalues")
#displaying a data frame with basic characteristics of the calculated iteration index values
$iterations
comparison#> # A tibble: 3 x 5
#> variable mean_iterations sd_iterations cv_iterations all_sample
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Jevons 1.02 0.00668 0.00655 1.02
#> 2 Fisher 0.987 0.00108 0.00110 0.987
#> 3 GEKS 0.988 0.000909 0.000921 0.988
```

```
#displaying a data frame with basic characteristics of the calculated index pseudovalues obtained in the jackknife procedure
$pseudovalues
comparison#> # A tibble: 3 x 4
#> variable jk_estimator sd_jk_estimator cv_jk
#> <fct> <dbl> <dbl> <dbl>
#> 1 Jevons 1.05 0.0267 0.0255
#> 2 Fisher 0.987 0.00433 0.00439
#> 3 GEKS 0.988 0.00364 0.00368
```

```
#displaying box-plotes created for the calculated iteration index values
$figure_iterations comparison
```

```
#displaying box-plotes created for the calculated index pseudovalues obtained in the jackknife procedure
$figure_pseudovalues comparison
```

There are four package functions for calculating price and quantity
indicators. The **bennet** function returns the (bilateral)
Bennet price and quantity indicators and optionally also the price and
quantity contributions of individual products. The
**mbennet** function returns the multilateral (transitive)
Bennet price and quantity indicators and optionally also the price and
quantity contributions of individual products. The
**montgomery** function returns the (bilateral) Montgomery
price and quantity indicators and optionally also the price and quantity
contributions of individual products. The **mmontgomery**
function returns the multilateral (transitive) Montgomery price and
quantity indicators and optionally also the price and quantity
contributions of individual products.For instance, the following command
calculates the Bennet price and quantity indicators for milk
products:

```
bennet(milk, start = "2018-12", end = "2019-12", interval=TRUE)
#> time Value_difference Price_indicator Quantity_indicator
#> 1 2019-01 -31942.53 628.05 -32570.58
#> 2 2019-02 -35995.09 -175.29 -35819.80
#> 3 2019-03 -42158.05 -3810.15 -38347.90
#> 4 2019-04 -56934.44 -2427.25 -54507.20
#> 5 2019-05 -50961.52 -2580.91 -48380.61
#> 6 2019-06 -48842.58 -2396.05 -46446.53
#> 7 2019-07 -33974.27 -3232.63 -30741.64
#> 8 2019-08 -37962.80 4500.45 -42463.26
#> 9 2019-09 -33833.42 -1092.32 -32741.09
#> 10 2019-10 -35001.60 -1665.10 -33336.50
#> 11 2019-11 -16928.94 2313.87 -19242.81
#> 12 2019-12 9859.34 -2151.48 12010.83
```

where price and quantity contributions of each subgroups of milk products can be obtained as follows:

```
$prodID<-milk$description
milkbennet(milk, start = "2018-12", end = "2019-12", contributions = TRUE)
#> prodID value_differences price_contributions
#> 1 full-fat milk UHT 8767.34 -1927.29
#> 2 full-fat milk pasteurized -711.57 -633.65
#> 3 goat milk -602.29 -4.10
#> 4 low-fat milk UHT -1525.62 369.49
#> 5 low-fat milk pasteurized 1421.39 647.66
#> 6 powdered milk 2510.09 1444.46
#> quantity_contributions
#> 1 10694.63
#> 2 -77.92
#> 3 -598.18
#> 4 -1895.11
#> 5 773.73
#> 6 1065.63
```

The following command calculates the Montgomery price and quantity indicators for coffee products:

```
montgomery(coffee, start = "2018-12", end = "2019-12", interval=TRUE)
#> time Value_difference Price_indicator Quantity_indicator
#> 1 2019-01 -468907.15 -9147.81 -459759.34
#> 2 2019-02 -494284.67 20407.49 -514692.16
#> 3 2019-03 -397279.68 -14075.89 -383203.79
#> 4 2019-04 -354810.23 18916.05 -373726.28
#> 5 2019-05 -504512.39 35906.94 -540419.33
#> 6 2019-06 -461707.07 132177.82 -593884.89
#> 7 2019-07 -423952.45 110250.18 -534202.63
#> 8 2019-08 -275624.60 126281.93 -401906.53
#> 9 2019-09 -346025.72 151139.77 -497165.49
#> 10 2019-10 -310279.89 135645.97 -445925.86
#> 11 2019-11 -260821.56 44782.99 -305604.55
#> 12 2019-12 8945.14 75463.07 -66517.93
```

where price and quantity contributions of each subgroups of coffee products can be obtained as follows:

```
$prodID<-coffee$description
coffeemontgomery(coffee, start = "2018-12", end = "2019-12", contributions = TRUE)
#> prodID value_differences price_contributions quantity_contributions
#> 1 coffee beans 121932.78 -6100.99 128033.77
#> 2 ground coffee -70172.42 -14307.11 -55865.31
#> 3 instant coffee -42815.22 14483.76 -57298.98
```