### Install HaploVar from GitHub

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Install the development version of HaploVar from GitHub using the devtools package. Ensure devtools is installed first.

```r
#install.packages("devtools")
#devtools::install_github("TessaMacNish/HaploVar")
```

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

### Install HaploVar Package

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Installs the development version of HaploVar from GitHub or directly from CRAN. Requires the devtools package for GitHub installation.

```r
install.packages("devtools")
devtools::install_github("TessaMacNish/HaploVar")
```

```r
install.packages("HaploVar")
```

```r
library(HaploVar)
```

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

### Complete GWAS Pipeline Example

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Demonstrates a full workflow for GWAS analysis using HaploVar, including VCF preprocessing and haplotype definition. This example uses built-in data for demonstration purposes.

```r
library(HaploVar)

data("vcf")
data("LD")
```

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

### Install HaploVar from CRAN

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Install the HaploVar package directly from CRAN. This is the recommended method for stable releases.

```r
install.packages("HaploVar")
```

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

### Install HaploVar

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Install the package from CRAN or the development version from GitHub.

```r
# Install from CRAN
install.packages("HaploVar")

# Or install development version from GitHub
install.packages("devtools")
devtools::install_github("TessaMacNish/HaploVar")

library(HaploVar)
```

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

### Load HaploVar Package

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Load the HaploVar package into your R session to access its functions. This should be done after installation.

```r
library(HaploVar)
```

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

### Read VCF and LD Matrix in R

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Reads VCF and LD matrix files into R using the crosshap package. Ensure crosshap is installed and loaded.

```r
install.packages("crosshap")
library(crosshap)
VCF <- read_vcf("vcf")
LD <- read_LD("output.ld",vcf = VCF)
```

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

### define_haplotypes

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Defines local haplotypes from VCF and LD matrix inputs using DBSCAN clustering. Returns a list of haplotype tables where each table contains SNP genotypes for all individuals within that haplotype. Haplotypes are named based on their genomic start and end positions.

```APIDOC
## define_haplotypes

### Description
Defines local haplotypes from VCF and LD matrix inputs using DBSCAN clustering. Returns a list of haplotype tables where each table contains SNP genotypes for all individuals within that haplotype. Haplotypes are named based on their genomic start and end positions.

### Method
R function

### Endpoint
N/A (R function)

### Parameters
#### Arguments
- **vcf** (data.frame) - VCF file with SNP genotypes.
- **LD** (matrix) - Linkage disequilibrium matrix.
- **epsilon** (numeric) - DBSCAN density parameter (0-1, higher = larger haplotypes). Default is 0.6.
- **MGmin** (integer) - Minimum SNPs required for a cluster to form a haplotype.
- **hetmiss_as** (character) - How to treat heterozygous missing alleles. Options: "allele" (treat as heterozygous), "missing" (treat as missing).
- **keep_outliers** (logical) - Whether to remove outlier SNPs.

### Request Example
```r
library(HaploVar)

# Load example data
data("vcf")  # VCF file with SNP genotypes
data("LD")   # Linkage disequilibrium matrix

# Define haplotypes with custom parameters
haplotype_list <- define_haplotypes(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,
  MGmin = 30,
  hetmiss_as = "allele",
  keep_outliers = FALSE
)

# Access individual haplotype tables
haplotype_table <- haplotype_list[[1]]
print(names(haplotype_list))  # Shows haplotype names like "hap_1234_5678"
head(haplotype_table[, 1:5])
```

### Response
#### Success Response
A list of haplotype tables. Each table contains SNP genotypes for all individuals within a specific haplotype. Haplotypes are named based on their genomic start and end positions (e.g., "hap_1234_5678").

#### Response Example
```r
# Output: Each row is a SNP, each column is an individual
#          R4155_R4155 R4156_R4156 R4157_R4157 R4158_R4158 R4159_R4159
# snp_001         0|0         0|1         0|0         1|1         0|0
# snp_002         0|0         0|1         0|0         1|1         0|0
```
```

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

### Genomic Selection Pipeline Workflow

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Full workflow for defining haplotypes and generating marker matrices for genomic selection models.

```r
library(HaploVar)
data("vcf")
data("LD")

# Step 1: Define haplotypes with optimized parameters
# Test different epsilon values for your data
for (eps in c(0.6, 0.7, 0.8, 0.9)) {
  haps <- define_haplotypes(vcf, LD, epsilon = eps)
  print(paste("epsilon =", eps, ":", length(haps), "haplotypes"))
}

# Step 2: Generate genomic selection format (format 3)
gs_data <- haplotype_variants(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,
  MGmin = 30,
  minFreq = 2,
  format = 3  # Transposed dosage: individuals as rows
)

# Step 3: Save marker matrix for genomic selection
write.csv(
  gs_data,
  "genomic_selection_markers.csv",
  row.names = TRUE,
  quote = FALSE
)

# Step 4: Use with genomic selection tools
# Example with rrBLUP:
# library(rrBLUP)
# marker_matrix <- as.matrix(gs_data)
# phenotypes <- read.csv("phenotypes.csv")
# model <- mixed.solve(y = phenotypes$trait, Z = marker_matrix)
# gebv <- marker_matrix %*% model$u  # Genomic estimated breeding values
```

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

### Define Haplotypes Globally

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Runs `define_haplotypes` on multiple VCF and LD matrix file pairs and collates the results. Requires lists of VCF files, LD matrices, and a list of epsilon values, one for each VCF file.

```R
vcf2 <- vcf
vcf_list <- list(vcf, vcf2)
LD2 <- LD
LD_list <- list(LD, LD2)
epsilon_list <- c(0.8, 0.8)
haplotype_list_global <- define_haplotypes_globally(vcf_list, LD_list, epsilon = epsilon_list)
```

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

### Define Haplotypes using HaploVar

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Run the `define_haplotypes` function with a VCF and LD matrix. The epsilon parameter controls haplotype density and should be tuned for your data.

```r
haplotype_list <- define_haplotypes(vcf, LD, epsilon = 0.8) #this produces a list of haplotype tables
```

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

### Inspect Haplotype Data Formats

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Display the structure of different haplotype data formats using the head function.

```R
head(format4, c(5,6))
```

```R
head(format5, c(5,3))
```

```R
head(format6, c(5,10))
```

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

### Display Head of Haplotype Table

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Shows the first 5 rows and 5 columns of a haplotype table. This table contains all SNPs within a defined haplotype and their genotypes.

```r
head(haplotype_table, c(5,5))
```

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

### Display Head of LD Matrix

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Displays the first 5 rows and 5 columns of an LD (Linkage Disequilibrium) matrix. This format is required for haplotype calculations.

```r
head(LD, c(5,5))
```

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

### Identify Haplotype Variants (Format 1)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Defines local haplotypes and identifies variants, outputting in format 1, which is the default. This format shows haplotype variants represented by letters (A-ZZ) for each haplotype, with '0' indicating absence.

```R
format1 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 1)
head(format1, c(5,6))
```

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

### Identify Haplotype Variants (Format 3)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Identifies haplotype variants and formats output for genomic selection tools.

```R
format3 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 3)
head(format3, c(5,3))
```

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

### Identify Haplotype Variants (Format 5)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Identifies haplotype variants and formats output in format 5.

```R
format5 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 5)
```

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

### Identify Haplotype Variants (Format 6)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Identifies haplotype variants and formats output in format 6.

```R
format6 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 6)
```

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

### Identify Haplotype Variants (Format 4)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Identifies haplotype variants and formats output in format 4.

```R
format4 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 4)
```

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

### Format Haplotype Variants for Genomic Prediction

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Identifies haplotype variants and formats the output for genomic selection pipelines (format = 3).

```r
format3 <- haplotype_variants(VCF, LD, epsilon = 0.8, format = 3)
```

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

### Collate Define Haplotypes Results

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Use this function to combine results from multiple `define_haplotypes` runs into a single list of haplotype tables. Ensure inputs are lists of VCF files and corresponding LD matrices.

```R
haplotype_list2 <- haplotype_list
list_outputs <- list(haplotype_list, haplotype_list2)
collate_haplotype_list <- collate_define_haplotypes(list_outputs)
```

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

### Identify Haplotypes Variants Globally

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Run haplotype_variants across a list of VCF files, LD matrices, and epsilon values using the haplotype_variants_global function.

```R
format1_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 1)
format2_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 2)
format3_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 3)
format4_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 4)
format5_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 5)
format6_global <- haplotype_variants_global(vcf_list, LD_list, epsilon = epsilon_list, format = 6)
```

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

### Define Haplotypes Globally

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Process multiple VCF files and LD matrices across genomic regions.

```r
library(HaploVar)
data("vcf")
data("LD")
```

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

### Define Local Haplotypes

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Use define_haplotypes to identify haplotype groups based on VCF and LD matrix inputs.

```r
library(HaploVar)

# Load example data
data("vcf")  # VCF file with SNP genotypes
data("LD")   # Linkage disequilibrium matrix

# Define haplotypes with custom parameters
# epsilon: DBSCAN density parameter (0-1, higher = larger haplotypes)
# MGmin: minimum SNPs required for a cluster to form a haplotype
haplotype_list <- define_haplotypes(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,      # Default is 0.6
  MGmin = 30,         # Minimum SNPs per haplotype
  hetmiss_as = "allele",  # Treat missing alleles as heterozygous
  keep_outliers = FALSE   # Remove outlier SNPs
)

# Access individual haplotype tables
haplotype_table <- haplotype_list[[1]]
print(names(haplotype_list))  # Shows haplotype names like "hap_1234_5678"
head(haplotype_table[, 1:5])
```

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

### Define Haplotypes Globally

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Use define_haplotypes_globally to process multiple VCF files and LD matrices for genome-wide haplotype analysis. Ensure epsilon values are optimized per region.

```r
vcf_chr1 <- vcf
vcf_chr2 <- vcf
vcf_list <- list(vcf_chr1, vcf_chr2)

LD_chr1 <- LD
LD_chr2 <- LD
LD_list <- list(LD_chr1, LD_chr2)

epsilon_list <- c(0.8, 0.7)

haplotype_list_global <- define_haplotypes_globally(
  vcf_list = vcf_list,
  LD_list = LD_list,
  epsilon = epsilon_list,
  MGmin = 30,
  hetmiss_as = "allele",
  keep_outliers = FALSE
)

print(length(haplotype_list_global))
print(names(haplotype_list_global))
```

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

### Define Haplotypes

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Calculates local haplotypes from VCF and LD data. The epsilon parameter controls the threshold for haplotype definition.

```r
haplotype_list <- define_haplotypes(VCF, LD, epsilon = 0.8)
```

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

### Display Head of VCF Data

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Shows the first 5 rows and 10 columns of a VCF data structure, illustrating the expected format for biallelic SNPs.

```r
head(vcf, c(5,10))
```

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

### Calculate Linkage Disequilibrium with PLINK

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Calculates the linkage disequilibrium (LD) matrix from VCF data using PLINK. Ensure the output is square.

```bash
plink --allow-extra-chr --r2 square --vcf vcf --out output.ld
```

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

### Format Haplotype Variants for GWAS

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Formats haplotype variants for GWAS (format = 6) and saves the output to a VCF file.

```r
write.table(format6, "output.vcf", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
```

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

### define_haplotypes_globally

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Processes multiple VCF files and LD matrices across chromosomes or genomic regions. Allows different epsilon values for each region and returns a combined list of all haplotype tables.

```APIDOC
## define_haplotypes_globally

### Description
Processes multiple VCF files and LD matrices across chromosomes or genomic regions. Allows different epsilon values for each region and returns a combined list of all haplotype tables.

### Method
R function

### Endpoint
N/A (R function)

### Parameters
#### Arguments
- **vcf_list** (list) - A list of VCF data frames, one for each chromosome or region.
- **LD_list** (list) - A list of LD matrices, corresponding to the VCF files.
- **epsilon_list** (list or numeric) - A list of epsilon values, one for each chromosome/region, or a single numeric value to apply globally.
- **MGmin** (integer) - Minimum SNPs required for a cluster to form a haplotype.
- **hetmiss_as** (character) - How to treat heterozygous missing alleles. Options: "allele" (treat as heterozygous), "missing" (treat as missing).
- **keep_outliers** (logical) - Whether to remove outlier SNPs.

### Request Example
```r
library(HaploVar)
data("vcf")
data("LD")

# Assuming vcf and LD are already loaded and potentially split by chromosome
# For demonstration, we'll use the same data multiple times
vcf_list <- list(vcf, vcf)
LD_list <- list(LD, LD)
epsilon_list <- c(0.7, 0.9) # Different epsilon for each region

combined_haplotypes <- define_haplotypes_globally(
  vcf_list = vcf_list,
  LD_list = LD_list,
  epsilon_list = epsilon_list,
  MGmin = 25,
  hetmiss_as = "missing",
  keep_outliers = TRUE
)

# Accessing results
print(names(combined_haplotypes)) # Shows names for haplotypes across all regions
```

### Response
#### Success Response
A single list containing all defined haplotype tables from all processed VCF files and LD matrices. Haplotypes are named to indicate their origin (e.g., chromosome/region and position).

#### Response Example
```r
# Example output structure (names will vary based on input data)
# [[1]]
#   snp_001 snp_002 snp_003
# R4155_R4155     0|0     0|1     0|0
# R4156_R4156     0|1     0|0     1|0
# 
# [[2]]
#   snp_004 snp_005
# R4155_R4155     1|1     0|0
# R4156_R4156     0|0     1|1
```
```

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

### Extract First Haplotype Table

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Extracts the first haplotype table from the list generated by `define_haplotypes`. Each table is named by the position of its first and last SNP.

```r
haplotype_table <- haplotype_list[[1]] #this is the first haplotype tables
```

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

### haplotype_variants

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Defines local haplotypes and identifies haplotype variants for each individual. Outputs formatted data compatible with GWAS and genomic selection tools. Supports six output formats for different downstream analysis requirements.

```APIDOC
## haplotype_variants

### Description
Defines local haplotypes and identifies haplotype variants for each individual. Outputs formatted data compatible with GWAS and genomic selection tools. Supports six output formats for different downstream analysis requirements.

### Method
R function

### Endpoint
N/A (R function)

### Parameters
#### Arguments
- **vcf** (data.frame) - VCF file with SNP genotypes.
- **LD** (matrix) - Linkage disequilibrium matrix.
- **epsilon** (numeric) - DBSCAN density parameter (0-1, higher = larger haplotypes). Default is 0.6.
- **MGmin** (integer) - Minimum SNPs required for a cluster to form a haplotype.
- **minFreq** (integer) - Minimum number of individuals with a variant for it to be included.
- **hetmiss_as** (character) - How to treat heterozygous missing alleles. Options: "allele" (treat as heterozygous), "missing" (treat as missing).
- **keep_outliers** (logical) - Whether to remove outlier SNPs.
- **format** (integer) - The desired output format (1-6).

### Request Example
```r
library(HaploVar)
data("vcf")
data("LD")

# Format 1: Default haplotype variant labels (A, B, C, etc.)
format1 <- haplotype_variants(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,
  MGmin = 30,
  minFreq = 2,
  hetmiss_as = "allele",
  keep_outliers = FALSE,
  format = 1
)
head(format1[, 1:6])

# Format 2: Dosage format for GWAS (0, 1, 2 copies)
format2 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 2)
head(format2[, 1:6])

# Format 3: Transposed dosage for genomic selection (individuals as rows)
format3 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 3)
head(format3[, 1:3])

# Format 4: Centered dosage for GWAS (-1, 0, 1)
format4 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 4)

# Format 5: Transposed centered dosage for genomic selection
format5 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 5)

# Format 6: VCF-compatible format for GWAS tools
format6 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 6)
head(format6[, 1:10])

# Save output for downstream analysis
write.csv(format3, "genomic_selection_input.csv", row.names = TRUE, quote = FALSE)
write.table(format6, "gwas_input.vcf", sep = "\t", row.names = FALSE, quote = FALSE)
```

### Response
#### Success Response
Returns data formatted according to the specified `format` argument. The structure varies based on the format:
- **Format 1**: Haplotype variant labels (e.g., A, B, C).
- **Format 2**: Dosage format (0, 1, 2 copies) for GWAS.
- **Format 3**: Transposed dosage (individuals as rows) for genomic selection.
- **Format 4**: Centered dosage (-1, 0, 1) for GWAS.
- **Format 5**: Transposed centered dosage for genomic selection.
- **Format 6**: VCF-compatible format for GWAS tools.

#### Response Example
```r
# Example for Format 1:
#        MARKER CHROM    POS R4155_R4155 R4156_R4156 R4157_R4157
# hap_1234_5678   C01  1234         A|A         A|B         A|A

# Example for Format 6:
#  #CHROM  POS              ID REF ALT QUAL FILTER INFO FORMAT R4155_R4155
#     C01 1234 hap_1234_5678_A   .   .    .   PASS    .     GT         1|1
```
```

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

### Identify Haplotype Variants (Format 2)

Source: https://github.com/tessamacnish/haplovar/blob/main/vignettes/introduction.html

Identifies haplotype variants and formats output for GWAS tools. Numbers represent the count of each haplotype variant (0, 1, or 2 copies).

```R
format2 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 2)
head(format2, c(5,6))
```

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

### Identify Haplotype Variants

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Generate formatted haplotype variant data for downstream GWAS or genomic selection tools using six different output formats.

```r
library(HaploVar)
data("vcf")
data("LD")

# Format 1: Default haplotype variant labels (A, B, C, etc.)
format1 <- haplotype_variants(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,
  MGmin = 30,
  minFreq = 2,           # Minimum individuals with variant
  hetmiss_as = "allele",
  keep_outliers = FALSE,
  format = 1
)
head(format1[, 1:6])

# Format 2: Dosage format for GWAS (0, 1, 2 copies)
format2 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 2)
head(format2[, 1:6])

# Format 3: Transposed dosage for genomic selection (individuals as rows)
format3 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 3)
head(format3[, 1:3])

# Format 4: Centered dosage for GWAS (-1, 0, 1)
format4 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 4)

# Format 5: Transposed centered dosage for genomic selection
format5 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 5)

# Format 6: VCF-compatible format for GWAS tools
format6 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 6)
head(format6[, 1:10])

# Save output for downstream analysis
write.csv(format3, "genomic_selection_input.csv", row.names = TRUE, quote = FALSE)
write.table(format6, "gwas_input.vcf", sep = "\t", row.names = FALSE, quote = FALSE)
```

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

### Define and Export Haplotypes for GWAS

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Defines haplotypes from VCF and LD data, then exports them in VCF format for GWAS tools.

```r
haplotype_list <- define_haplotypes(vcf, LD, epsilon = 0.8)
print(paste("Found", length(haplotype_list), "haplotypes"))

# Step 4: Generate GWAS-compatible output (format 6 = VCF)
gwas_input <- haplotype_variants(
  vcf = vcf,
  LD = LD,
  epsilon = 0.8,
  MGmin = 30,
  minFreq = 2,
  format = 6
)

# Step 5: Save for GWAS analysis
write.table(
  gwas_input,
  "haplotype_gwas.vcf",
  sep = "\t",
  row.names = FALSE,
  col.names = TRUE,
  quote = FALSE
)
```

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

### Save Formatted Haplotype Variants

Source: https://github.com/tessamacnish/haplovar/blob/main/README.md

Saves the formatted haplotype variants to a CSV file for use in genomic selection pipelines.

```r
write.csv(format3, "output.csv", row.names=TRUE, quote = FALSE )
```

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

### Generate Haplotype Variants Globally

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Utilize haplotype_variants_global to generate genome-wide haplotype variants in various formats. Specify the desired format (1, 2, 3, or 6) for the output table.

```r
library(HaploVar)
data("vcf")
data("LD")

vcf_list <- list(vcf, vcf)
LD_list <- list(LD, LD)
epsilon_list <- c(0.8, 0.8)

format1_global <- haplotype_variants_global(
  vcf_list = vcf_list,
  LD_list = LD_list,
  epsilon = epsilon_list,
  MGmin = 30,
  minFreq = 2,
  format = 1
)

format2_global <- haplotype_variants_global(vcf_list, LD_list, epsilon_list, format = 2)
format3_global <- haplotype_variants_global(vcf_list, LD_list, epsilon_list, format = 3)
format6_global <- haplotype_variants_global(vcf_list, LD_list, epsilon_list, format = 6)

write.csv(format3_global, "genome_wide_genomic_selection.csv", row.names = TRUE)
write.table(format6_global, "genome_wide_gwas.vcf", sep = "\t", row.names = FALSE)
```

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

### Collate Haplotype Variants

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Combine multiple haplotype variant tables into a single table using collate_haplotype_variants. Ensure the format parameter matches the format used for the input tables.

```r
library(HaploVar)
data("vcf")
data("LD")

format1_chr1 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 1)
format1_chr2 <- haplotype_variants(vcf, LD, epsilon = 0.7, format = 1)

format3_chr1 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 3)
format3_chr2 <- haplotype_variants(vcf, LD, epsilon = 0.7, format = 3)

format6_chr1 <- haplotype_variants(vcf, LD, epsilon = 0.8, format = 6)
format6_chr2 <- haplotype_variants(vcf, LD, epsilon = 0.7, format = 6)

format1_list <- list(format1_chr1, format1_chr2)
format1_combined <- collate_haplotype_variants(format1_list, format = 1)

format3_list <- list(format3_chr1, format3_chr2)
format3_combined <- collate_haplotype_variants(format3_list, format = 3)

format6_list <- list(format6_chr1, format6_chr2)
format6_combined <- collate_haplotype_variants(format6_list, format = 6)

write.csv(format3_combined, "combined_genomic_selection.csv", row.names = TRUE)
write.table(format6_combined, "combined_gwas.vcf", sep = "\t", row.names = FALSE)
```

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

### Collate Define Haplotypes Results

Source: https://context7.com/tessamacnish/haplovar/llms.txt

Use collate_define_haplotypes to combine multiple haplotype lists generated from separate calls to define_haplotypes. This is useful for merging results from different chromosomes or regions.

```r
library(HaploVar)
data("vcf")
data("LD")

haplotype_list_chr1 <- define_haplotypes(vcf, LD, epsilon = 0.8)
haplotype_list_chr2 <- define_haplotypes(vcf, LD, epsilon = 0.7)
haplotype_list_chr3 <- define_haplotypes(vcf, LD, epsilon = 0.8)

list_of_haplotype_lists <- list(
  haplotype_list_chr1,
  haplotype_list_chr2,
  haplotype_list_chr3
)

all_haplotypes <- collate_define_haplotypes(list_of_haplotype_lists)

print(length(all_haplotypes))
print(names(all_haplotypes))
```

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