diff --git a/.gitignore b/.gitignore
index 52065156e3ac726b62fa0dc22c64f03ee791f2b6..566cf9944d46563554e6016cf46ef9669a369458 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,16 +1,16 @@
-# For checking that no file has been unduly ignored, run:
-# $ git ls-files -i --exclude-per-directory=.gitignore
-# Any file that is displayed should be removed from the ignore list
-# (possibly by an exclusion rule beginning with an exclamation mark)
-
-# Generic ignore rules
-*~
-*.o
-*.a
-*.fig
-\#*\#
-*.mat
-*.asv
-
-# Test directory
-m-unit-tests
+# For checking that no file has been unduly ignored, run:
+# $ git ls-files -i --exclude-per-directory=.gitignore
+# Any file that is displayed should be removed from the ignore list
+# (possibly by an exclusion rule beginning with an exclamation mark)
+
+# Generic ignore rules
+*~
+*.o
+*.a
+*.fig
+\#*\#
+*.mat
+*.asv
+
+# Test directory
+m-unit-tests
diff --git a/README.md b/README.md
index 1443471f823188122a3779176c7983880b8d9716..215fddb2cc501fd834c6b6e80491404b5e6fee95 100644
--- a/README.md
+++ b/README.md
@@ -1,179 +1,179 @@
-[](https://git.dynare.org/DoraK/mdbnomics/commits/master)
-
-This MATLAB/Octave toolbox comes with routines to access DBnomics time series from MATLAB.
-
-The package is compatible with MATLAB 2019b and following versions, and (almost compatible with)
-the latest Octave version.
-
-## Installation
-
-The toolbox can be installed by cloning the Git repository:
-
- ~$ git clone https://git.dynare.org/DoraK/mdbnomics.git
-
-or downloading a zip archive:
-
- ~$ wget https://git.dynare.org/DoraK/mdbnomics/-/archive/master/mdbnomics-master.zip
- ~$ unsip mdbnomics-master.zip
- -$ mv mdbnomics-master mdbnomics
-
-## Usage
-
-Add the `mdbnomics/src` folder to the MATLAB/Octave path, and run the following command (on MATLAB/Octave) prompt:
-
- >> initialize_mdbnomics()
-
-which, depending on your system, will add the necessary subfolders to
-the MATLAB/Octave path.
-
-You are then ready to go. A full documentation will come soon.
-
-## Capabilities
-
-### Fetch one time series by ID
-First, let's assume that we know which series we want to download.
-A series identifier (ID) is defined by three values, formatted like this: `provider_code/dataset_code/series_code`.
-The `fetch_series` function is used to construct the cell array.
-For example, to fetch the time series `EA19.1.0.0.0.ZUTN` from the
-[\"Unemployment rate\" [ZUTN] dataset](https://db.nomics.world/AMECO/ZUTN)
-belonging to the [AMECO provider](https://db.nomics.world/AMECO).
-
-Example:
-
- >> df_id = fetch_series('series_ids', 'AMECO/ZUTN/EA19.1.0.0.0.ZUTN');
-
-The returned data is stored in the `df_id` variable. Its type is a cell array. To display the first 3 rows of the array
-(including the column headers), type:
-
- >> df_id(1:4,:)
-
- 6×16 cell array
-
- Columns 1 through 5
-
- {'x_frequency'} {'provider_code'} {'dataset_code'} {'dataset_name' } {'series_code' }
- {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra…'} {'EA19.1.0.0.0.ZUTN'}
- {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra…'} {'EA19.1.0.0.0.ZUTN'}
- {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra…'} {'EA19.1.0.0.0.ZUTN'}
-
- Columns 6 through 11
-
- {'series_name' } {'original_period'} {'period' } {'original_value'} {'value'} {'freq'}
- {'Annually – (Per…'} {'1960' } {'1960-01-01'} {'NA' } {[ NaN]} {'a' }
- {'Annually – (Per…'} {'1961' } {'1961-01-01'} {'NA' } {[ NaN]} {'a' }
- {'Annually – (Per…'} {'1962' } {'1962-01-01'} {'NA' } {[ NaN]} {'a' }
-
- Columns 12 through 16
-
- {'unit' } {'geo' } {'Frequency'} {'Unit' } {'Country' }
- {'percentage-of-a…'} {'ea19'} {'Annually' } {'(Percentage of …'} {'Euro area'}
- {'percentage-of-a…'} {'ea19'} {'Annually' } {'(Percentage of …'} {'Euro area'}
- {'percentage-of-a…'} {'ea19'} {'Annually' } {'(Percentage of …'} {'Euro area'}
-
- >>
-
-In such cell array, you will always find at least those columns:
-* `x_frequency`: (harmonized frequency generated by DBnomics)
-* `provider_code`
-* `dataset_code`
-* `dataset_name`
-* `series_code`
-* `series_name`
-* `original_period`: the `period` as returned by DBnomics
-* `period`: the first day of `original_period`
-* `original_value` (`str` or `float`): the observation value as returned by DBnomics, where not available values are represented by `'NA'`
-* `value` (`float` or `NaN`): the observation value as returned by DBnomics, where not available values are represented by `NaN`
-
-Followed by dimensions columns, corresponding to the dimensions of the dataset:
-* dimensions labels: `freq`, `unit`, `geo`
-* dimensions values labels: `Frequency`, `Unit`, `Country`
-
-### Fetch two time series by ID
-Again, let's assume that we know which series we want to download.
-We can reuse the `fetch_series` function, this time with two series codes.
-For example, to fetch the time series `EA19.1.0.0.0.ZUTN` and `DNK.1.0.0.0.ZUTN` from the
-[\"Unemployment rate\" [ZUTN] dataset](https://db.nomics.world/AMECO/ZUTN)
-belonging to the [AMECO provider](https://db.nomics.world/AMECO).
-
-Example:
-
- >> df_ids = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN'});
-
-### Fetch time series by code mask
-The code mask notation is a very concise way to select one or many time series at once.
-It is not compatible with all the providers. In particular, only the providers from the following list accept code mask:
-* BIS
-* ECB
-* Eurostat
-* FED
-* IMF
-* IMF-WEO
-* INSEE
-* OECD
-* WTO
-
-Given 3 dimensions 'frequency', 'country' and 'indicator', the user can select:
-* one time series by giving its code: `'M.FR.PCPIEC_IX'`
-* many series by enumerating dimensions codes: `'M.FR+DE.PCPIEC_IX'` is equivalent to `{'M.FR.PCPIEC_IX', 'M.DE.PCPIEC_IX'}`
-* many series by skipping a dimension, repeating '.' in the code mask: `'M..PCPIEC_IX'` is equivalent to `{'M.country1.PCPIEC_IX', 'M.country2.PCPIEC_IX', ..., 'M.countryN.PCPIEC_IX'}`
-
-Examples:
-
- >> df_code_mask1 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M.FR+DE.PCPIEC_IX+PCPIA_IX');
- >> df_code_mask2 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', '.FR.PCPIEC_WT');
- >> df_code_mask3 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M..PCPIEC_IX+PCPIA_IX', 'max_nb_series', 400);
-
-### Fetch time series by dimension
-Searching by dimension is a less concise way to select time series than using the code mask, but it's universal:
-some fetchers are not compatible with the code mask notation. The following example fetches many series from the
-["Doing Business" [DB]](https://db.nomics.world/WB/DB) dataset of the [World Bank](https://db.nomics.world/WB) provider, selecting for time series about France, Italy and Spain (`country` dimension),
-and the indicator "Procedures required to start a business - Women (number)" (`indicator` dimension).
-
-Example:
-
- >> df_dims = fetch_series('provider_code', 'WB', 'dataset_code', 'DB', 'dimensions', '{"country":["ES","FR","IT"],"indicator":["IC.REG.COST.PC.FE.ZS.DRFN"]}');
-
-### Fetch time series by API link
-When the dimensions, provider, dataset or series codes are unknown, the user can:
-* go to the page of a dataset on DBnomics website (eg: [Doing Business](https://db.nomics.world/WB/DB))
-* select some dimensions by using the input widgets of the left column
-* click on `Copy API link` in the menu of the `Download` button
-* use the `fetch_series_by_api_link` function
-
-Example:
-
- >> df_link = fetch_series_by_api_link('https://api.db.nomics.world/v22/series/WB/DB/ENF.CONT.COEN.ATDR-AE?observations=1');
-
-### Fetch time series from the cart
-On the [cart page](https://db.nomics.world/cart) of the DBnomics website, click on "Copy API link" and copy-paste it as an argument of the fetch_series_by_api_link function.
-Please note that when you update your cart, you have to copy this link again, because the link itself contains the IDs of the series in the cart.
-
-Example:
-
- >> df_cart = fetch_series_by_api_link('https://api.db.nomics.world/v22/series?series_ids=AMECO%2FZUTN%2FEA19.1.0.0.0.ZUTN&observations=1');
-
-### Fetch time series with different frequencies
-
-Example:
-
- >> df_multi_freq = fetch_series('series_ids', {'BEA/NIUnderlyingDetail-U001BC/S315-A',...
- 'BEA/NIUnderlyingDetail-U001BC/S315-Q',...
- 'BEA/NIUnderlyingDetail-U001BC/S315-M'});
-
-### Transform time series
-The routines can interact with the [Time Series Editor](https://editor.nomics.world/) to transform time series by applying filters to them.
-Available filters are listed on the [filters page](https://editor.nomics.world/filters).
-The Time Series Editor is usable via a web interface ([example with AMECO/ZUTN/EA19.1.0.0.0.ZUTN](https://editor.nomics.world/series?source=dbnomics&series_id=AMECO/ZUTN/EA19.1.0.0.0.ZUTN))
-but you can call it directly from MATLAB. The user is also able to chain many filters.
-Here is an example of how to interpolate two annual time series with a monthly frequency, using a spline interpolation.
-
-Example:
-
- >> filters_ = '[{"code": "interpolate", "parameters": {"frequency": "monthly", "method": "spline"}}]';
- >> df_filter = fetch_series('series_ids', 'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'dbnomics_filters', filters_);
-
-The first row of the final cell array changes when filters are used:
-* `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones)
-* `filtered` (`bool`): True if the series is filtered
-* `series_code`: same as before for original series, but the suffix `_filtered` is added for filtered series
-* `series_name`: same as before for original series, but the suffix `(filtered)` is added for filtered series
\ No newline at end of file
+[](https://git.dynare.org/DoraK/mdbnomics/commits/master)
+
+This MATLAB/Octave toolbox comes with routines to access DBnomics time series from MATLAB.
+
+The package is compatible with MATLAB 2019b and following versions, and (almost compatible with)
+the latest Octave version.
+
+## Installation
+
+The toolbox can be installed by cloning the Git repository:
+
+ ~$ git clone https://git.dynare.org/DoraK/mdbnomics.git
+
+or downloading a zip archive:
+
+ ~$ wget https://git.dynare.org/DoraK/mdbnomics/-/archive/master/mdbnomics-master.zip
+ ~$ unsip mdbnomics-master.zip
+ -$ mv mdbnomics-master mdbnomics
+
+## Usage
+
+Add the `mdbnomics/src` folder to the MATLAB/Octave path, and run the following command (on MATLAB/Octave) prompt:
+
+ >> initialize_mdbnomics()
+
+which, depending on your system, will add the necessary subfolders to
+the MATLAB/Octave path.
+
+You are then ready to go. A full documentation will come soon.
+
+## Capabilities
+
+### Fetch one time series by ID
+First, let's assume that we know which series we want to download.
+A series identifier (ID) is defined by three values, formatted like this: `provider_code/dataset_code/series_code`.
+The `fetch_series` function is used to construct the cell array.
+For example, to fetch the time series `EA19.1.0.0.0.ZUTN` from the
+[\"Unemployment rate\" [ZUTN] dataset](https://db.nomics.world/AMECO/ZUTN)
+belonging to the [AMECO provider](https://db.nomics.world/AMECO).
+
+Example:
+
+ >> df_id = fetch_series('series_ids', 'AMECO/ZUTN/EA19.1.0.0.0.ZUTN');
+
+The returned data is stored in the `df_id` variable. Its type is a cell array. To display the first 3 rows of the array
+(including the column headers), type:
+
+ >> df_id(1:4,:)
+
+ 6�16 cell array
+
+ Columns 1 through 5
+
+ {'x_frequency'} {'provider_code'} {'dataset_code'} {'dataset_name' } {'series_code' }
+ {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra�'} {'EA19.1.0.0.0.ZUTN'}
+ {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra�'} {'EA19.1.0.0.0.ZUTN'}
+ {'annual' } {'AMECO' } {'ZUTN' } {'Unemployment ra�'} {'EA19.1.0.0.0.ZUTN'}
+
+ Columns 6 through 11
+
+ {'series_name' } {'original_period'} {'period' } {'original_value'} {'value'} {'freq'}
+ {'Annually � (Per�'} {'1960' } {'1960-01-01'} {'NA' } {[ NaN]} {'a' }
+ {'Annually � (Per�'} {'1961' } {'1961-01-01'} {'NA' } {[ NaN]} {'a' }
+ {'Annually � (Per�'} {'1962' } {'1962-01-01'} {'NA' } {[ NaN]} {'a' }
+
+ Columns 12 through 16
+
+ {'unit' } {'geo' } {'Frequency'} {'Unit' } {'Country' }
+ {'percentage-of-a�'} {'ea19'} {'Annually' } {'(Percentage of �'} {'Euro area'}
+ {'percentage-of-a�'} {'ea19'} {'Annually' } {'(Percentage of �'} {'Euro area'}
+ {'percentage-of-a�'} {'ea19'} {'Annually' } {'(Percentage of �'} {'Euro area'}
+
+ >>
+
+In such cell array, you will always find at least those columns:
+* `x_frequency`: (harmonized frequency generated by DBnomics)
+* `provider_code`
+* `dataset_code`
+* `dataset_name`
+* `series_code`
+* `series_name`
+* `original_period`: the `period` as returned by DBnomics
+* `period`: the first day of `original_period`
+* `original_value` (`str` or `float`): the observation value as returned by DBnomics, where not available values are represented by `'NA'`
+* `value` (`float` or `NaN`): the observation value as returned by DBnomics, where not available values are represented by `NaN`
+
+Followed by dimensions columns, corresponding to the dimensions of the dataset:
+* dimensions labels: `freq`, `unit`, `geo`
+* dimensions values labels: `Frequency`, `Unit`, `Country`
+
+### Fetch two time series by ID
+Again, let's assume that we know which series we want to download.
+We can reuse the `fetch_series` function, this time with two series codes.
+For example, to fetch the time series `EA19.1.0.0.0.ZUTN` and `DNK.1.0.0.0.ZUTN` from the
+[\"Unemployment rate\" [ZUTN] dataset](https://db.nomics.world/AMECO/ZUTN)
+belonging to the [AMECO provider](https://db.nomics.world/AMECO).
+
+Example:
+
+ >> df_ids = fetch_series('series_ids', {'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'AMECO/ZUTN/DNK.1.0.0.0.ZUTN'});
+
+### Fetch time series by code mask
+The code mask notation is a very concise way to select one or many time series at once.
+It is not compatible with all the providers. In particular, only the providers from the following list accept code mask:
+* BIS
+* ECB
+* Eurostat
+* FED
+* IMF
+* IMF-WEO
+* INSEE
+* OECD
+* WTO
+
+Given 3 dimensions 'frequency', 'country' and 'indicator', the user can select:
+* one time series by giving its code: `'M.FR.PCPIEC_IX'`
+* many series by enumerating dimensions codes: `'M.FR+DE.PCPIEC_IX'` is equivalent to `{'M.FR.PCPIEC_IX', 'M.DE.PCPIEC_IX'}`
+* many series by skipping a dimension, repeating '.' in the code mask: `'M..PCPIEC_IX'` is equivalent to `{'M.country1.PCPIEC_IX', 'M.country2.PCPIEC_IX', ..., 'M.countryN.PCPIEC_IX'}`
+
+Examples:
+
+ >> df_code_mask1 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M.FR+DE.PCPIEC_IX+PCPIA_IX');
+ >> df_code_mask2 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', '.FR.PCPIEC_WT');
+ >> df_code_mask3 = fetch_series('provider_code', 'IMF', 'dataset_code', 'CPI', 'series_code', 'M..PCPIEC_IX+PCPIA_IX', 'max_nb_series', 400);
+
+### Fetch time series by dimension
+Searching by dimension is a less concise way to select time series than using the code mask, but it's universal:
+some fetchers are not compatible with the code mask notation. The following example fetches many series from the
+["Doing Business" [DB]](https://db.nomics.world/WB/DB) dataset of the [World Bank](https://db.nomics.world/WB) provider, selecting for time series about France, Italy and Spain (`country` dimension),
+and the indicator "Procedures required to start a business - Women (number)" (`indicator` dimension).
+
+Example:
+
+ >> df_dims = fetch_series('provider_code', 'WB', 'dataset_code', 'DB', 'dimensions', '{"country":["ES","FR","IT"],"indicator":["IC.REG.COST.PC.FE.ZS.DRFN"]}');
+
+### Fetch time series by API link
+When the dimensions, provider, dataset or series codes are unknown, the user can:
+* go to the page of a dataset on DBnomics website (eg: [Doing Business](https://db.nomics.world/WB/DB))
+* select some dimensions by using the input widgets of the left column
+* click on `Copy API link` in the menu of the `Download` button
+* use the `fetch_series_by_api_link` function
+
+Example:
+
+ >> df_link = fetch_series_by_api_link('https://api.db.nomics.world/v22/series/WB/DB/ENF.CONT.COEN.ATDR-AE?observations=1');
+
+### Fetch time series from the cart
+On the [cart page](https://db.nomics.world/cart) of the DBnomics website, click on "Copy API link" and copy-paste it as an argument of the fetch_series_by_api_link function.
+Please note that when you update your cart, you have to copy this link again, because the link itself contains the IDs of the series in the cart.
+
+Example:
+
+ >> df_cart = fetch_series_by_api_link('https://api.db.nomics.world/v22/series?series_ids=AMECO%2FZUTN%2FEA19.1.0.0.0.ZUTN&observations=1');
+
+### Fetch time series with different frequencies
+
+Example:
+
+ >> df_multi_freq = fetch_series('series_ids', {'BEA/NIUnderlyingDetail-U001BC/S315-A',...
+ 'BEA/NIUnderlyingDetail-U001BC/S315-Q',...
+ 'BEA/NIUnderlyingDetail-U001BC/S315-M'});
+
+### Transform time series
+The routines can interact with the [Time Series Editor](https://editor.nomics.world/) to transform time series by applying filters to them.
+Available filters are listed on the [filters page](https://editor.nomics.world/filters).
+The Time Series Editor is usable via a web interface ([example with AMECO/ZUTN/EA19.1.0.0.0.ZUTN](https://editor.nomics.world/series?source=dbnomics&series_id=AMECO/ZUTN/EA19.1.0.0.0.ZUTN))
+but you can call it directly from MATLAB. The user is also able to chain many filters.
+Here is an example of how to interpolate two annual time series with a monthly frequency, using a spline interpolation.
+
+Example:
+
+ >> filters_ = '[{"code": "interpolate", "parameters": {"frequency": "monthly", "method": "spline"}}]';
+ >> df_filter = fetch_series('series_ids', 'AMECO/ZUTN/EA19.1.0.0.0.ZUTN', 'dbnomics_filters', filters_);
+
+The first row of the final cell array changes when filters are used:
+* `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones)
+* `filtered` (`bool`): True if the series is filtered
+* `series_code`: same as before for original series, but the suffix `_filtered` is added for filtered series
+* `series_name`: same as before for original series, but the suffix `(filtered)` is added for filtered series