> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/computing/julia/juliastats.md).

# JuliaStats

JuliaStats contains basic statistics functionality, which can be used as the foundation for  statistics, machine learning, and data science needs. It is efficient, scalable, and reusable!

## Installation & Setup

JuliaStats is not a single package, but rather a suite of packages. Specific packages can be downloaded depending on your needs.

To begin, import the package manager and initialize your desired package with the following code.

```julia
import Pkg
Pkg.add(*package name*)

using *package name*
```

For example, if you wanted to download the `StatsBase` package, use the following code.

```julia
import Pkg
Pkg.add("StatsBase")

using StatsBase
```

## Commonly Used Packages

| Package                | Use                                                                        |
| ---------------------- | -------------------------------------------------------------------------- |
| `StatsBase.jl`         | Basic statistics, weights, sampling, counts, and summary statistics.       |
| `Distributions.jl`     | Probability distributions and related functions (PDF, CDF, sampling, etc). |
| `StatsModel.jl`        | Statistical model formulas                                                 |
| `GLM.jl`               | Generalized linear models (e.g., linear regression, logistic regression).  |
| `MixedModels.jl`       | Linear and generalized linear mixed-effects models.                        |
| `HypothesisTest.jl`    | Statistical hypothesis tests (t-tests, chi-squared, ANOVA, etc).           |
| `MultivariateStats.jl` | Multivariate analysis (PCA, factor analysis, ICA, etc).                    |

Please refer to each package's documentation for a list of available functions and their usage. 

## Example

```julia
# Using StatsBase
data = ..
mean_val = mean(data)
var_val = var(data)

# Using Distributions
pdf_val = pdf(Normal(0,1), 1)

# Using GLM
df = DataFrame(..)
model = lm(@formula(y ~ x), df)
```

## Resources

* [JuliaStats](https://github.com/JuliaStats)
* [StatsModels](https://juliastats.org/StatsModels.jl/stable/)


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