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## Installation
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You can install the stable version of *Rcatch22* from [CRAN](https://cran.r-project.org/web/packages/Rcatch22/readme/README.html) using the following:
```r
install.packages("Rcatch22")
```
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## Getting Started: Basic Usage
Here we outline how you can jump straight into computing *catch22* features on your time series data with a basic usage example.
### Expected Input
*Rcatch22* expects a **single univariate time series** as a **numeric** vector with length equal to the number of points in the time series. For example, consider a time series with length 100:
```r
library(Rcatch22)
data <- stats::rnorm(100) # your time series data
```
### Computing *catch22* features
To compute *catch22* features, call the `catch22_all` function on your time series data as follows:
```r
outs22 <- catch22_all(data)
```
### Expected output
A data frame is returned with two columns: `names` and `features` as shown below
```
outs22
## names values
## 1 DN_HistogramMode_5 0.432653377
## 2 DN_HistogramMode_10 0.174395817
## 3 CO_f1ecac 1.865299453
... ... ...
## 21 SP_Summaries_welch_rect_centroid 1.178097245
## 22 FC_LocalSimple_mean3_stderr 1.201213638
```
To access the full list of feature names, you can also run `feature_list.`
And that's it! You now have all the knowledge you need to apply *catch22* to your time series data. To access some of the additional functionality of *catch22*, keep reading for advanced usage tips and examples.
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## Advanced Usage
Ready to explore some of the additional functionality of *Rcatch22?*
### 1. *Catch24*
If the location and spread of the raw time series distribution may be important for your application, you can enable *catch24.*
{% tabs %}
{% tab title="Catch24" %}
*Catch24* is an extension of the original *catch22* feature set to include **mean** and **standard deviation,** yielding a total of **24** time series features. An option to include the mean and standard deviation as features in addition to *catch22* is available through setting the *catch24* argument to `TRUE`:
```r
library(Rcatch22)
x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
features <- catch22_all(x, catch24 = TRUE)
```
{% endtab %}
{% endtabs %}
### 2. Computing Individual Features
If you do not wish to compute the full *catch22* feature set on your time series, you can alternatively compute each feature (including mean and std. deviation) individually.
{% tabs %}
{% tab title="Computing Individual Features" %}
In *pycatch22* each feature function can be accessed individually and takes **arrays** as **tuples** or To compute individual features in *Rcatch22*, you can access the feature functions using their unique names (as shown in the [feature overview table](https://time-series-features.gitbook.io/catch22/information-about-catch22/feature-descriptions/feature-overview-table)). Note that feature functions are given by the feature long name e.g.,`IN_AutoMutualInfoStats_40_gaussian_fmmi`.
For example, let's compute the feature `IN_AutoMutualInfoStats_40_gaussian_fmmi` on a single univariate time series:
```r
library(Rcatch22)
x <- 1 + 0.5 * 1:1000 + arima.sim(list(ma = 0.5), n = 1000)
f <- IN_AutoMutualInfoStats_40_gaussian_fmmi(x)
```
{% endtab %}
{% endtabs %}
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## FAQ
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Click one of the expandable tabs below to explore commonly asked questions about *Rcatch22*:
How does computational time scale as a function of time series length in Rcatch22?
With features coded in C, *Rcatch22* is highly computationally efficient, scaling nearly linearly with time series size. Computation time in seconds for a range of time series lengths is presented [here](https://github.com/hendersontrent/Rcatch22?tab=readme-ov-file#computational-performance).
How can I submit a bug report for Rcatch22?
If you would like to submit a bug report, you can access our Bug Tracker [here](https://github.com/hendersontrent/Rcatch22/issues). For guidelines on submitting an ideal bug report, see our page on [contributing to *catch22*](https://time-series-features.gitbook.io/catch22/information-about-catch22/contributing-to-catch22)*.*
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