### Installation

Source: https://context7.com/wanglabcsu/faers/llms.txt

Instructions on how to install the faers package from Bioconductor.

```APIDOC
## Installation

Install from Bioconductor.

```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("faers")
```
```

--------------------------------

### Install FAERS Development Version from GitHub

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Install the latest development version of the faers package from GitHub using the 'pak' package manager. Ensure 'pak' is installed first.

```r
if (!requireNamespace("pak", quietly = TRUE)) {
    install.packages("pak", repos = "https://r-lib.github.io/p/pak/stable")
}
pak::pkg_install("WangLabCSU/faers")
```

--------------------------------

### Install FAERS from Bioconductor

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Install the stable version of the faers package from Bioconductor. Ensure BiocManager is installed first.

```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("faers")
```

--------------------------------

### faers_load() - Load Package Sample Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

Loads sample data bundled with the faers package for testing and examples.

```APIDOC
## faers_load() - Load Package Sample Data

### Description
Loads sample data bundled with the faers package for testing and examples.

### Method
`faers_load(name)`

### Parameters
#### Path Parameters
- **name** (character) - Required - The name of the sample dataset to load (e.g., "irAEs").

### Request Example
```r
# Load immune-related adverse events reference data
irAEs_data <- faers_load("irAEs")
```

### Response
#### Success Response (200)
- Returns a data.table containing the specified sample dataset.

#### Response Example
```r
#    pt_code             pt_name                    class
# 1: 10000636    Adrenal insufficiency    Endocrine disorders
# 2: 10001551              Alopecia     Skin disorders
# ...
```
```

--------------------------------

### Get MedDRA Version

Source: https://context7.com/wanglabcsu/faers/llms.txt

Retrieves the version number of the loaded MedDRA dictionary.

```r
# Get MedDRA version
meddra_version(meddra_data)
# [1] "26.0"

```

--------------------------------

### FAERS PHV Signal Analysis Example

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Demonstrates the use of `faers_phv_signal` to detect signals for 'hlgt_name' events from 'insulin_data'. The function performs various statistical tests including ROR, PRR, and EBGM.

```R
insulin_signals_hlgt <- faers_phv_signal(
    insulin_data,
    .events = "hlgt_name", .full = data,
    BPPARAM = BiocParallel::SerialParam(RNGseed = 1L)
)
#> ℹ Running `phv_ror()`
#> ℹ Running `phv_prr()`
#> ℹ Running `phv_chisq()`
#> ℹ Running `phv_bcpnn_norm()`
#> ℹ Running `phv_bcpnn_mcmc()`
#> ℹ Running `phv_obsexp_shrink()`
#> ℹ Running `phv_fisher()`
#> ℹ Running `phv_ebgm()`
insulin_signals_hlgt
#> Key: <hlgt_name>
#>                                                       hlgt_name     a     b
#>                                                          <char> <int> <int>
#>   1:                                   Abortions and stillbirth     0     9
#>   2:                                        Acid-base disorders     0     9
#>   3:                              Administration site reactions     0     9
#>   4:                                        Allergic conditions     1     8
#>   5:               Anaemias nonhaemolytic and marrow depression     1     8
#>  ---                                                                       
#> 136:                                 Viral infectious disorders     0     9
#> 137:                                           Vision disorders     0     9
#> 138:                                  Vitamin related disorders     0     9
#> 139: Vulvovaginal disorders (excl infections and inflammations)     0     9
#> 140:                                 White blood cell disorders     0     9
#>          c     d expected       ror ror_ci_low ror_ci_high       prr prr_ci_low
#>      <int> <int>    <num>     <num>      <num>       <num>     <num>      <num>
#>   1:     1   190    0.045  0.000000  0.0000000         NaN  0.000000  0.0000000
#>   2:     1   190    0.045  0.000000  0.0000000         NaN  0.000000  0.0000000
#>   3:     7   184    0.315  0.000000  0.0000000         NaN  0.000000  0.0000000
#>   4:     3   188    0.180  7.833333  0.7313919    83.89635  7.074074  0.8140454
#>   5:     2   189    0.135 11.812500  0.9671714   144.27139 10.611111  1.0580416
#>  ---                                                                           
#> 136:     5   186    0.225  0.000000  0.0000000         NaN  0.000000  0.0000000
#> 137:     2   189    0.090  0.000000  0.0000000         NaN  0.000000  0.0000000
#> 138:     1   190    0.045  0.000000  0.0000000         NaN  0.000000  0.0000000

```

--------------------------------

### Get FAERS Data by Reaction

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Use faers_get to retrieve FAERS data filtered by reaction codes. Requires the 'data' object to be pre-loaded.

```R
faers_get(data, "reac")
```

--------------------------------

### Get FAERS Data by Drug

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Use faers_get to retrieve FAERS data filtered by drug information. Requires the 'data' object to be pre-loaded.

```R
faers_get(data, "drug")
```

--------------------------------

### Get FAERS Metadata (Internal Copy)

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Retrieves FAERS metadata using an internal copy associated with the package. This is useful for offline access or consistent results.

```R
faers_meta(internal = TRUE)
```

--------------------------------

### Get FAERS Metadata

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Retrieves FAERS metadata, defaulting to cached data. If cache is unavailable, it parses metadata from the official FDA website.

```R
faers_meta()
```

--------------------------------

### Download and Load FDA Drugs Database with fda_drugs()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Downloads and parses the FDA Drugs@FDA database. Can list available files, load default 'Products.txt', load files matching a pattern, or force a refresh from the FDA.

```r
# List available files in Drugs@FDA dataset
fda_drugs(list = TRUE)
# [1] "Products.txt"  "Submissions.txt" ...

# Load Products data (default)
products <- fda_drugs()
#    ApplNo ProductNo Form Strength ReferenceDrug DrugName ...

# Load specific file pattern
submissions <- fda_drugs(pattern = "Submissions")

# Force refresh from FDA
products <- fda_drugs(force = TRUE)

```

--------------------------------

### faers_meta() - List Available FAERS Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

Lists metadata for all FAERS databases currently available for download, including years, quarters, file URLs, and file sizes.

```APIDOC
## faers_meta() - List Available FAERS Data

Lists metadata for all FAERS databases currently available for download, including years, quarters, file URLs, and file sizes.

```r
# Get FAERS metadata (uses cache by default)
metadata <- faers_meta()
#     year quarter         period                          ascii_urls ascii_file_size ...
# 1:  2024      q3 July - September https://fis.fda.gov/.../faers_ascii_2024q3.zip          82MB
# 2:  2024      q2    April - June  https://fis.fda.gov/.../faers_ascii_2024q2.zip          79MB
# ...

# Force refresh metadata from FDA website
metadata <- faers_meta(force = TRUE)

# Use internal cached metadata (no internet required)
metadata <- faers_meta(internal = TRUE)
```
```

--------------------------------

### Check data availability with faers_available()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Verifies if FAERS data for specific years and quarters are available for download.

```r
# Check single year/quarter combination
faers_available(2023, "q1")
# [1] TRUE

# Check multiple combinations
faers_available(c(2011, 2023), c("q1", "q2"))
# [1] TRUE TRUE

# Check future/unavailable data
faers_available(2030, "q1")
# [1] FALSE
```

--------------------------------

### Load MedDRA Dictionary with meddra()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Loads MedDRA (Medical Dictionary for Regulatory Activities) data. Optionally includes Standardised MedDRA Queries (SMQ). Requires a valid MedDRA subscription and path.

```r
# Load MedDRA hierarchy data
meddra_data <- meddra("/path/to/MedDRA")
# Hierarchy data for MedDRA (version 26.0)

# Include SMQ (Standardised MedDRA Queries)
meddra_data <- meddra("/path/to/MedDRA", add_smq = TRUE)
# Hierarchy and SMQs data for MedDRA (version 26.0)
```

--------------------------------

### Load FAERS Package Sample Data with faers_load()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Loads sample data bundled with the faers package, such as immune-related adverse events (irAEs) reference data, useful for filtering and testing.

```r
# Load immune-related adverse events reference data
irAEs <- faers_load("irAEs")
#    pt_code             pt_name                    class
# 1: 10000636    Adrenal insufficiency    Endocrine disorders
# 2: 10001551              Alopecia     Skin disorders
# ...

# This data is useful for filtering ICI-related adverse events

```

--------------------------------

### Load faers library

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Load the faers package into the R session to begin pharmacovigilance analysis.

```R
library(faers)
```

--------------------------------

### faers_download() - Download FAERS Data Files

Source: https://context7.com/wanglabcsu/faers/llms.txt

Downloads FAERS data for selected years and quarters without parsing. Useful when you want to download first and parse later.

```APIDOC
## faers_download() - Download FAERS Data Files

Downloads FAERS data for selected years and quarters without parsing. Useful when you want to download first and parse later.

```r
# Download ASCII format (default)
files <- faers_download(
    years = 2023,
    quarters = c("q1", "q2"),
    dir = "./faers_data"
)
# Finding 2 files already downloaded: faers_ascii_2023q1.zip, faers_ascii_2023q2.zip
# or
# Downloading 2 files

# Download XML format (only available from 2012q4 onwards)
files <- faers_download(
    years = 2023,
    quarters = "q1",
    format = "xml",
    dir = "./faers_data"
)
```
```

--------------------------------

### faers_available() - Check Data Availability

Source: https://context7.com/wanglabcsu/faers/llms.txt

Check if FAERS data for specific years and quarters are available for download.

```APIDOC
## faers_available() - Check Data Availability

Check if FAERS data for specific years and quarters are available for download.

```r
# Check single year/quarter combination
faers_available(2023, "q1")
# [1] TRUE

# Check multiple combinations
faers_available(c(2011, 2023), c("q1", "q2"))
# [1] TRUE TRUE

# Check future/unavailable data
faers_available(2030, "q1")
# [1] FALSE
```
```

--------------------------------

### Commit Changes

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Stage and commit your code changes with a descriptive message.

```git
git commit -m 'Add some AmazingFeature'
```

--------------------------------

### Create a New Feature Branch

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Use this command to create a new branch for your feature development.

```git
git checkout -b feature/AmazingFeature
```

--------------------------------

### Load FAERS Data with faers()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Loads FAERS data for a specified year and quarter. Requires specifying the directory and an optional compression directory.

```r
data <- faers(2004, "q1",
    dir = system.file("extdata", package = "faers"),
    compress_dir = tempdir()
)
```

--------------------------------

### Run Disproportionality Analysis with phv_signal

Source: https://context7.com/wanglabcsu/faers/llms.txt

Performs disproportionality analysis using contingency table values. Can run all methods or specific ones like ROR and PRR. Requires a, b, c, d counts.

```r
# Using contingency table values: a=n11, b=n10, c=n01, d=n00
# a: event of interest under exposure of interest
# b: not event of interest under exposure of interest
# c: event of interest under not exposure of interest
# d: not event of interest under not exposure of interest

# Run all methods
results <- phv_signal(a = 122, b = 1320, c = 381, d = 31341)
#    expected      ror ror_ci_low ror_ci_high     prr prr_ci_low prr_ci_high ...
# 1:    42.34  7.89       6.23        9.98    6.71       5.58        8.07

# Run specific methods
ror_result <- phv_signal(122, 1320, 381, 31341, methods = "ror")
prr_result <- phv_signal(122, 1320, 381, 31341, methods = "prr")
```

--------------------------------

### Download FAERS files with faers_download()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Downloads raw FAERS data files without parsing. Supports both ASCII and XML formats.

```r
# Download ASCII format (default)
files <- faers_download(
    years = 2023,
    quarters = c("q1", "q2"),
    dir = "./faers_data"
)
# Finding 2 files already downloaded: faers_ascii_2023q1.zip, faers_ascii_2023q2.zip
# or
# Downloading 2 files

# Download XML format (only available from 2012q4 onwards)
files <- faers_download(
    years = 2023,
    quarters = "q1",
    format = "xml",
    dir = "./faers_data"
)
```

--------------------------------

### Download and Parse Single FAERS Quarterly Data

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Downloads and parses a single quarterly data file. Ensure the directory path is correctly specified for your environment.

```R
# Please make sure to replace dir with your own directory path, as the file
    # included in the package is a sampled version.
    data1 <- faers(2004, "q1",
        dir = system.file("extdata", package = "faers"),
        compress_dir = tempdir()
    )
    #>
    data1
    #>
```

--------------------------------

### Basic FAERS Pharmacovigilance Workflow

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Demonstrates a basic pharmacovigilance analysis for Aspirin using the faers package. This includes downloading and parsing data, standardization, deduplication, and signal detection. Ensure MedDRA dictionary is available for standardization.

```r
library(faers)

# 1. Download and Parse Data (2023 Q1-Q2)
# Note: Ensure you have enough disk space in the target directory
data <- faers(2023, c("q1", "q2"), dir = "./faers_data")

# 2. Standardization (Requires MedDRA dictionary)
data_stand <- faers_standardize(data, meddra_path = "path/to/MedDRA")

# 3. Deduplication (Requires Standardized data)
data_dedup <- faers_dedup(data_stand)

# 4.Signal Detection (Data screening for items of interest is needed, such as "aspirin".)
results <- faers_phv_signal(
    faers_filter(data_dedup, .fn = ~ drugname == "aspirin"),
    .full = data_dedup
)
```

--------------------------------

### Retrieve FAERS metadata with faers_meta()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Lists available FAERS databases, including URLs and file sizes. Supports refreshing from the FDA website or using internal cached data.

```r
# Get FAERS metadata (uses cache by default)
metadata <- faers_meta()
#     year quarter         period                          ascii_urls ascii_file_size ...
# 1:  2024      q3 July - September https://fis.fda.gov/.../faers_ascii_2024q3.zip          82MB
# 2:  2024      q2    April - June  https://fis.fda.gov/.../faers_ascii_2024q2.zip          79MB
# ...

# Force refresh metadata from FDA website
metadata <- faers_meta(force = TRUE)

# Use internal cached metadata (no internet required)
metadata <- faers_meta(internal = TRUE)
```

--------------------------------

### meddra() - Load MedDRA Dictionary

Source: https://context7.com/wanglabcsu/faers/llms.txt

Loads MedDRA (Medical Dictionary for Regulatory Activities) data for terminology standardization. Requires a valid MedDRA subscription.

```APIDOC
## meddra() - Load MedDRA Dictionary

### Description
Loads MedDRA (Medical Dictionary for Regulatory Activities) data for terminology standardization. Requires a valid MedDRA subscription.

### Method
`meddra(path, add_smq = FALSE)`

### Parameters
#### Path Parameters
- **path** (character) - Required - The file path to the MedDRA data directory.
- **add_smq** (logical) - Optional - Whether to include Standardised MedDRA Queries (SMQ) data. Defaults to FALSE.

### Request Example
```r
# Load MedDRA hierarchy data
meddra_data <- meddra("/path/to/MedDRA")

# Include SMQ (Standardised MedDRA Queries)
meddra_data_with_smq <- meddra("/path/to/MedDRA", add_smq = TRUE)
```

### Response
#### Success Response (200)
- Returns a list containing MedDRA hierarchy and optionally SMQ data.

#### Response Example
```
# Hierarchy data for MedDRA (version 26.0)
# Hierarchy and SMQs data for MedDRA (version 26.0)
```

## Helper functions for MedDRA data

### meddra_hierarchy()

#### Description
Extracts hierarchy data from the loaded MedDRA object.

#### Method
`meddra_hierarchy(meddra_object)`

#### Response Example
```r
#    llt_code           llt_name pt_code              pt_name ...
```

### meddra_smq()

#### Description
Extracts SMQ data from the loaded MedDRA object (if `add_smq` was TRUE).

#### Method
`meddra_smq(meddra_object)`

#### Response Example
```r
# SMQ data table
```

### meddra_version()

#### Description
Retrieves the version of the loaded MedDRA data.

#### Method
`meddra_version(meddra_object)`

#### Response Example
```r
# [1] "26.0"
```
```

--------------------------------

### Parse FAERS files with faers_parse()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Parses downloaded FAERS zip files or directories into R objects. Supports ASCII and XML formats.

```r
# Parse from zip file
data <- faers_parse(
    path = "./faers_data/faers_ascii_2023q1.zip",
    compress_dir = tempdir()
)

# Parse from uncompressed directory
data <- faers_parse(
    path = "./faers_data/ascii/",
    format = "ascii",
    year = 2023,
    quarter = "q1"
)

# Parse sample data included in the package
data <- faers_parse(
    system.file("extdata", "aers_ascii_2004q1.zip", package = "faers"),
    compress_dir = tempdir()
)
```

--------------------------------

### faers_parse() - Parse Downloaded FAERS Files

Source: https://context7.com/wanglabcsu/faers/llms.txt

Parses FAERS quarterly data from downloaded zip files or uncompressed directories. Supports both ASCII and XML formats.

```APIDOC
## faers_parse() - Parse Downloaded FAERS Files

Parses FAERS quarterly data from downloaded zip files or uncompressed directories. Supports both ASCII and XML formats.

```r
# Parse from zip file
data <- faers_parse(
    path = "./faers_data/faers_ascii_2023q1.zip",
    compress_dir = tempdir()
)

# Parse from uncompressed directory
data <- faers_parse(
    path = "./faers_data/ascii/",
    format = "ascii",
    year = 2023,
    quarter = "q1"
)

# Parse sample data included in the package
data <- faers_parse(
    system.file("extdata", "aers_ascii_2004q1.zip", package = "faers"),
    compress_dir = tempdir()
)
```
```

--------------------------------

### faers() - Download and Parse FAERS Quarterly Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

The main entry point function that downloads and parses FAERS quarterly data files in a single call. It handles both the download and parsing process automatically, combining multiple quarters when specified.

```APIDOC
## faers() - Download and Parse FAERS Quarterly Data

The main entry point function that downloads and parses FAERS quarterly data files in a single call. It handles both the download and parsing process automatically, combining multiple quarters when specified.

```r
library(faers)

# Download and parse a single quarter
data <- faers(2023, "q1", dir = "./faers_data")

# Download and parse multiple quarters
data <- faers(2023, c("q1", "q2"), dir = "./faers_data")

# Download multiple years and quarters
data <- faers(c(2022, 2023), c("q4", "q1"), dir = "./faers_data")

# Use existing downloaded files (from package samples)
data <- faers(
    2004,
    "q1",
    dir = system.file("extdata", package = "faers"),
    compress_dir = tempdir()
)
# FAERS data from 1 Quarterly ascii file
#   Total reports: 100 (with duplicates)
```
```

--------------------------------

### phv_signal() - Low-Level Disproportionality Analysis

Source: https://context7.com/wanglabcsu/faers/llms.txt

Performs disproportionality analysis on contingency table values directly. Useful for custom analyses or when working with pre-computed counts.

```APIDOC
## phv_signal() - Low-Level Disproportionality Analysis

### Description
Performs disproportionality analysis on contingency table values directly. Useful for custom analyses or when working with pre-computed counts.

### Method
`phv_signal(a, b, c, d, methods = "all", ...)`

### Parameters
#### Path Parameters
- **a** (numeric) - Required - Event of interest under exposure of interest (n11)
- **b** (numeric) - Required - Not event of interest under exposure of interest (n10)
- **c** (numeric) - Required - Event of interest under not exposure of interest (n01)
- **d** (numeric) - Required - Not event of interest under not exposure of interest (n00)
- **methods** (character vector) - Optional - Methods to run (e.g., "ror", "prr", "bcpnn_norm", "ebgm"). Defaults to "all".

### Request Example
```r
# Using contingency table values: a=n11, b=n10, c=n01, d=n00
# Run all methods
results <- phv_signal(a = 122, b = 1320, c = 381, d = 31341)

# Run specific methods
ror_result <- phv_signal(122, 1320, 381, 31341, methods = "ror")
```

### Response
#### Success Response (200)
- Returns a data.table with disproportionality metrics (e.g., expected, ror, prr) and their confidence intervals.

#### Response Example
```
#    expected      ror ror_ci_low ror_ci_high     prr prr_ci_low prr_ci_high 
# 1:    42.34  7.89       6.23        9.98    6.71       5.58        8.07
```

## Individual Method Functions

### phv_ror()

#### Description
Calculates the Reporting Odds Ratio (ROR).

#### Method
`phv_ror(a, b, c, d)`

#### Response Example
```
#        ror   ci_low  ci_high
# 1: 7.8903    6.2276   9.9987
```

### phv_prr()

#### Description
Calculates the Proportional Reporting Ratio (PRR).

#### Method
`phv_prr(a, b, c, d)`

#### Response Example
```
#        prr   ci_low  ci_high
# 1: 6.7125    5.5787   8.0785
```

### phv_bcpnn_norm()

#### Description
Calculates the Bayesian Confidence Propagation Neural Network (BCPNN) normalized score.

#### Method
`phv_bcpnn_norm(a, b, c, d)`

#### Response Example
```
#        ic   ci_low  ci_high
# 1: 2.6801   2.2891   3.0711
```

### phv_ebgm()

#### Description
Calculates the Empirical Bayes Geometric Mean (EBGM).

#### Method
`phv_ebgm(a, b, c, d)`

#### Response Example
```
#     ebgm   ci_low  ci_high
# 1: 2.879    2.392    3.451
```
```

--------------------------------

### athena() - Access ATHENA Drug Vocabularies

Source: https://context7.com/wanglabcsu/faers/llms.txt

Reads and parses ATHENA VOCABULARIES data for drug standardization using OMOP vocabulary concepts.

```APIDOC
## athena() - Access ATHENA Drug Vocabularies

### Description
Reads and parses ATHENA VOCABULARIES data for drug standardization using OMOP vocabulary concepts.

### Method
`athena(use, url = NULL, ...)`

### Parameters
#### Path Parameters
- **use** (character or character vector) - Required - Specifies which vocabulary file(s) to load (e.g., "concept", "domain", "concept_synonym").
- **url** (character) - Optional - The URL to download the ATHENA data from. Required on the first run if data is not cached.
- **...** - Additional arguments passed to underlying functions.

### Request Example
```r
# List available vocabularies
vocab_files <- athena(list = TRUE)

# Load concept data (requires URL on first run)
concept_data <- athena(
    use = "concept",
    url = "https://athena.ohdsi.org/..." # Replace with actual URL
)

# Load multiple vocabulary files
vocab_data <- athena(use = c("concept", "concept_synonym"))

# Use cached data (if previously downloaded)
vocab_data_cached <- athena(use = "concept")
```

### Response
#### Success Response (200)
- Returns a data.table (or a list of data.tables if multiple `use` values are provided) containing the requested ATHENA vocabulary data.

#### Response Example
```r
# concept data table
# concept_synonym data table
```
```

--------------------------------

### Download and parse FAERS data with faers()

Source: https://context7.com/wanglabcsu/faers/llms.txt

The primary function for downloading and parsing FAERS quarterly data in one step. It supports single or multiple quarters and years, and can utilize local files.

```r
library(faers)

# Download and parse a single quarter
data <- faers(2023, "q1", dir = "./faers_data")

# Download and parse multiple quarters
data <- faers(2023, c("q1", "q2"), dir = "./faers_data")

# Download multiple years and quarters
data <- faers(c(2022, 2023), c("q4", "q1"), dir = "./faers_data")

# Use existing downloaded files (from package samples)
data <- faers(2004, "q1",
    dir = system.file("extdata", package = "faers"),
    compress_dir = tempdir()
)
# FAERS data from 1 Quarterly ascii file
#   Total reports: 100 (with duplicates)
```

--------------------------------

### Load Standardized FAERS Data

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Reads a pre-standardized FAERS dataset from the package's internal storage.

```R
data <- readRDS(system.file("extdata", "standardized_data.rds", package = "faers"))
data
```

--------------------------------

### Access ATHENA Drug Vocabularies with athena()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Reads and parses ATHENA VOCABULARIES data for drug standardization. Can list available vocabularies, load specific ones (e.g., 'concept'), multiple files, or use cached data.

```r
# List available vocabularies
athena(list = TRUE)
# [1] "concept.csv" "domain.csv" "concept_class.csv" ...

# Load concept data (requires URL on first run)
concept_data <- athena(
    use = "concept",
    url = "https://athena.ohdsi.org/..." # Your download URL
)

# Load multiple vocabulary files
vocab_data <- athena(use = c("concept", "concept_synonym"))

# Use cached data
vocab_data <- athena(use = "concept")  # Uses previously cached data

```

--------------------------------

### faers_combine() - Combine Multiple FAERS Objects

Source: https://context7.com/wanglabcsu/faers/llms.txt

Combines multiple FAERSascii or FAERSxml objects from different quarterly files into a single object. Objects must not be de-duplicated before combining.

```APIDOC
## faers_combine() - Combine Multiple FAERS Objects

Combines multiple FAERSascii or FAERSxml objects from different quarterly files into a single object. Objects must not be de-duplicated before combining.

```r
# Parse two separate quarterly files
data1 <- faers_parse(
    system.file("extdata", "aers_ascii_2004q1.zip", package = "faers"),
    compress_dir = tempdir()
)
data2 <- faers_parse(
    system.file("extdata", "faers_ascii_2017q2.zip", package = "faers"),
    compress_dir = tempdir()
)

# Combine into single object
combined_data <- faers_combine(data1, data2)
# Combining all 2 FAERS Datas
# FAERS data from 2 Quarterly ascii files
```
```

--------------------------------

### fda_drugs() - Access FDA Drug Database

Source: https://context7.com/wanglabcsu/faers/llms.txt

Downloads and parses the FDA Drugs@FDA database for drug product information.

```APIDOC
## fda_drugs() - Access FDA Drug Database

### Description
Downloads and parses the FDA Drugs@FDA database for drug product information.

### Method
`fda_drugs(list = FALSE, pattern = NULL, force = FALSE)`

### Parameters
#### Path Parameters
- **list** (logical) - Optional - If TRUE, lists available files in the dataset instead of downloading. Defaults to FALSE.
- **pattern** (character) - Optional - A pattern to filter the files to download/load. If NULL, loads the default 'Products.txt'.
- **force** (logical) - Optional - If TRUE, forces a refresh of the data from the FDA website. Defaults to FALSE.

### Request Example
```r
# List available files in Drugs@FDA dataset
file_list <- fda_drugs(list = TRUE)

# Load Products data (default)
products_data <- fda_drugs()

# Load specific file pattern (e.g., Submissions)
submissions_data <- fda_drugs(pattern = "Submissions")

# Force refresh from FDA
products_refreshed <- fda_drugs(force = TRUE)
```

### Response
#### Success Response (200)
- Returns a data.table containing the requested FDA drug information.

#### Response Example
```r
#    ApplNo ProductNo Form Strength ReferenceDrug DrugName ...
```
```

--------------------------------

### Extract Multiple Fields from FAERS Data with faers_mget()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Extracts multiple specified fields from FAERS data simultaneously. Returns a list containing the requested data tables.

```r
# Extract multiple fields at once
fields <- faers_mget(data, c("demo", "drug", "reac"))
# $demo
#     year quarter primaryid caseid ...
# $drug
#     year quarter primaryid drug_seq drugname ...
# $reac
#     year quarter primaryid pt ...

```

--------------------------------

### faers_phv_signal()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Performs disproportionality analysis for drug safety signal detection.

```APIDOC
## faers_phv_signal()

### Description
Performs disproportionality analysis for drug safety signal detection. Supports multiple statistical methods including ROR, PRR, BCPNN, and EBGM.

### Parameters
- **data** (object) - Required - The drug-specific data to analyze.
- **.full** (object) - Optional - The full database for comparison.
- **.object2** (object) - Optional - A second drug dataset for direct comparison.
- **.methods** (vector) - Optional - Specific statistical methods to run (e.g., c('ror', 'prr', 'ebgm')).
```

--------------------------------

### Combine Multiple FAERS Quarterly Data Files

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Uses faers_combine internally to process and merge multiple quarterly data files.

```R
data2 <- faers(c(2004, 2017), c("q1", "q2"),
        dir = system.file("extdata", package = "faers"),
        compress_dir = tempdir()
    )
    #>
    data2
    #>
```

--------------------------------

### Combine FAERS objects with faers_combine()

Source: https://context7.com/wanglabcsu/faers/llms.txt

Merges multiple FAERS objects into a single object. Objects must not be de-duplicated prior to combination.

```r
# Parse two separate quarterly files
data1 <- faers_parse(
    system.file("extdata", "aers_ascii_2004q1.zip", package = "faers"),
    compress_dir = tempdir()
)
data2 <- faers_parse(
    system.file("extdata", "faers_ascii_2017q2.zip", package = "faers"),
    compress_dir = tempdir()
)

# Combine into single object
combined_data <- faers_combine(data1, data2)
# Combining all 2 FAERS Datas
# FAERS data from 2 Quarterly ascii files
```

--------------------------------

### Check FAERS Metadata

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Retrieves metadata for FAERS data, including years, periods, quarters, and file URLs/sizes. By default, it uses a cached file. If the cache is not found, it parses the metadata from the official FDA website.

```APIDOC
## GET /faers_meta

### Description
Retrieves metadata for FAERS data, including years, periods, quarters, and file URLs/sizes. By default, it uses a cached file. If the cache is not found, it parses the metadata from the official FDA website.

### Method
GET

### Endpoint
/faers_meta

### Parameters
#### Query Parameters
- **internal** (boolean) - Optional - If TRUE, uses the internal FAERS metadata snapshot associated with the package.

### Response
#### Success Response (200)
- **data.table** - A data.table containing the following columns:
  - **year** (integer) - The year of the FAERS data.
  - **quarter** (character) - The quarter of the FAERS data (e.g., 'q1', 'q2').
  - **period** (character) - The time period corresponding to the quarter (e.g., 'January - March').
  - **ascii_urls** (character) - The URL to download the ASCII version of the FAERS data file.
  - **ascii_file_size** (character) - The size of the ASCII FAERS data file (e.g., '63.9MB').

### Response Example
```json
{
  "data.table": [
    {
      "year": 2024,
      "quarter": "q2",
      "period": "April - June",
      "ascii_urls": "https://fis.fda.gov/content/Exports/faers_ascii_2024q2.zip",
      "ascii_file_size": "63.9MB"
    },
    {
      "year": 2024,
      "quarter": "q1",
      "period": "January - March",
      "ascii_urls": "https://fis.fda.gov/content/Exports/faers_ascii_2024q1.zip",
      "ascii_file_size": "63.1MB"
    }
  ]
}
```
```

--------------------------------

### Retrieve MedDRA Data Components

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Accesses MedDRA hierarchy or SMQ data components within the standardized FAERS object.

```R
faers_meddra(data)
faers_meddra(data, use = "hierarchy")
```

--------------------------------

### Push Changes to Branch

Source: https://github.com/wanglabcsu/faers/blob/devel/README.md

Push your committed changes to your remote feature branch.

```git
git push origin feature/AmazingFeature
```

--------------------------------

### Standardize FAERS Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

Standardizes adverse event terms using MedDRA. Requires a valid MedDRA license and path.

```r
# Load FAERS data
data <- faers(c(2004, 2017), c("q1", "q2"),
    dir = system.file("extdata", package = "faers"),
    compress_dir = tempdir()
)

# Standardize with MedDRA (requires MedDRA license)
data_std <- faers_standardize(data, meddra_path = "/path/to/MedDRA")
# standardize Preferred Term in indi
# standardize Preferred Term in reac

# Include SMQ (Standardised MedDRA Queries) data
data_std <- faers_standardize(data,
    meddra_path = "/path/to/MedDRA",
    add_smq = TRUE
)
```

--------------------------------

### Perform pharmacovigilance signal analysis

Source: https://github.com/wanglabcsu/faers/blob/devel/README.html

Executes signal detection methods on filtered FAERS data using faers_phv_signal.

```R
insulin_signals <- faers_phv_signal(insulin_data,
    .full = data,
    BPPARAM = BiocParallel::SerialParam(RNGseed = 1L)
)
#> ℹ Running `phv_ror()`
#> ℹ Running `phv_prr()`
#> ℹ Running `phv_chisq()`
#> ℹ Running `phv_bcpnn_norm()`
#> ℹ Running `phv_bcpnn_mcmc()`
#> ℹ Running `phv_obsexp_shrink()`
#> ℹ Running `phv_fisher()`
#> ℹ Running `phv_ebgm()`
insulin_signals
#> Key: <soc_name>
#>                                                                soc_name     a
#>                                                                  <char> <int>
#>  1:                                Blood and lymphatic system disorders     1
#>  2:                                                   Cardiac disorders     2
#>  3:                          Congenital, familial and genetic disorders     0
#>  4:                                         Ear and labyrinth disorders     0
#>  5:                                                 Endocrine disorders     0
#>  6:                                                       Eye disorders     0
#>  7:                                          Gastrointestinal disorders     1
#>  8:                General disorders and administration site conditions     3
#>  9:                                             Hepatobiliary disorders     0
#> 10:                                             Immune system disorders     1
#> 11:                                         Infections and infestations     1
#> 12:                      Injury, poisoning and procedural complications     2
#> 13:                                                      Investigations     5
#> 14:                                  Metabolism and nutrition disorders     2
#> 15:                     Musculoskeletal and connective tissue disorders     2
#>         b     c     d expected       ror ror_ci_low ror_ci_high       prr
#>     <int> <int> <int>    <num>     <num>      <num>       <num>     <num>
#>  1:     8    10   181    0.495 2.2625000 0.25725215   19.898400 2.1222222
#>  2:     7    14   177    0.720 3.6122449 0.68476430   19.055189 3.0317460
#>  3:     9     1   190    0.045 0.0000000 0.00000000         NaN 0.0000000
#>  4:     9     1   190    0.045 0.0000000 0.00000000         NaN 0.0000000
```

--------------------------------

### Combine FAERS Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

Combines multiple FAERS data objects into a single list.

```r
combined_data <- faers_combine(list(data1, data2))
```

--------------------------------

### Detect Pharmacovigilance Signals

Source: https://context7.com/wanglabcsu/faers/llms.txt

Performs disproportionality analysis using various statistical methods.

```r
# Prepare data: standardize and deduplicate
data <- faers(c(2022, 2023), c("q3", "q4"), dir = "./faers_data")
data_std <- faers_standardize(data, meddra_path = "/path/to/MedDRA")
data_dedup <- faers_dedup(data_std)

# Filter for drug of interest
aspirin_data <- faers_filter(data_dedup,
    .fn = ~ .x[toupper(drugname) == "ASPIRIN"]$primaryid,
    .field = "drug"
)

# Run signal detection comparing to full database
results <- faers_phv_signal(aspirin_data, .full = data_dedup)
# Running phv_ror
# Running phv_prr
# Running phv_chisq
# Running phv_bcpnn_norm
# Running phv_bcpnn_mcmc
# Running phv_obsexp_shrink
# Running phv_fisher
# Running phv_ebgm

# Results include all disproportionality measures
#                    soc_name   a   b    c      d expected   ror ror_ci_low ror_ci_high ...
# 1: Gastrointestinal disorders 122 1320  381 31341     42.3  7.89       6.23        9.98

# Run specific methods only
results <- faers_phv_signal(aspirin_data,
    .full = data_dedup,
    .methods = c("ror", "prr", "ebgm")
)

# Compare two drugs against each other
drug_a <- faers_filter(data_dedup, .fn = ~ .x[drugname == "DRUG_A"]$primaryid, .field = "drug")
drug_b <- faers_filter(data_dedup, .fn = ~ .x[drugname == "DRUG_B"]$primaryid, .field = "drug")
comparison <- faers_phv_signal(drug_a, .object2 = drug_b)
```

--------------------------------

### faers_get() and faers_mget() - Extract Field Data

Source: https://context7.com/wanglabcsu/faers/llms.txt

Extract specific field data from FAERS objects. Available fields are: demo, drug, indi, ther, reac, rpsr, and outc.

```APIDOC
## faers_get() and faers_mget() - Extract Field Data

### Description
Extract specific field data from FAERS objects. Available fields are: demo, drug, indi, ther, reac, rpsr, and outc.

### Method
`faers_get(data, field)`
`faers_mget(data, fields)`

### Parameters
#### Path Parameters
- **data** (list or data.table) - Required - The FAERS data object loaded by `faers()`.
- **field** (character) - Required - The name of the field to extract (e.g., "demo", "drug").
- **fields** (character vector) - Required - A vector of field names to extract.

### Request Example
```r
data <- faers(2004, "q1",
    dir = system.file("extdata", package = "faers"),
    compress_dir = tempdir()
)

# Extract single field
demo_data <- faers_get(data, "demo")
drug_data <- faers_get(data, "drug")

# Alternative syntax using $ or [[]]
demo_data <- data$demo
reac_data <- data[["reac"]]

# Extract multiple fields at once
fields <- c("demo", "drug", "reac")
multi_fields_data <- faers_mget(data, fields)
```

### Response
#### Success Response (200)
- `faers_get()` returns a data.table for the specified field.
- `faers_mget()` returns a list of data.tables for the specified fields.

#### Response Example
```r
# $demo
#     year quarter primaryid caseid ...
# $drug
#     year quarter primaryid drug_seq drugname ...
# $reac
#     year quarter primaryid pt ...
```
```

--------------------------------

### Calculate EBGM with phv_ebgm

Source: https://context7.com/wanglabcsu/faers/llms.txt

Calculates the Empirical Bayesian Geometric Mean (EBGM) for disproportionality analysis. Returns EBGM and its confidence interval.

```r
phv_ebgm(122, 1320, 381, 31341)
#     ebgm   ci_low  ci_high
# 1: 2.879    2.392    3.451
```