diff --git a/NAMESPACE b/NAMESPACE
index e4b4354..e56cca0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -15,6 +15,7 @@ export(get_convergence)
 export(get_lineage_info)
 export(get_mutation_defs)
 export(get_res)
+export(parse_ivar)
 export(parse_mutations)
 export(pos_from_aa)
 export(provoc)
diff --git a/R/provoc_methods.R b/R/provoc_methods.R
index bfddfc5..a74da86 100644
--- a/R/provoc_methods.R
+++ b/R/provoc_methods.R
@@ -1,4 +1,3 @@
-
 #' Predict using Proportions of Variants of Concern
 #'
 #' Takes a named list with an estimate of the proportions and the
@@ -6,6 +5,8 @@
 #' predict outcomes, and returns results in the same order as the original data.
 #'
 #' @param provoc_obj Named list with `proportions` and `variant_matrix`.
+#' @param b1 Name of the first sra sample group.
+#' @param b2 Name of the second sra sample group.
 #' @param newdata Not yet implemented.
 #' @param type Not yet implemented.
 #' @param se.fit Not yet implemented.
@@ -13,24 +14,36 @@
 #' @param terms Not yet implemented.
 #' @param na.action Not yet implemented.
 #' @return Predicted values in the same order as the input data.
-#' @export
 #' @examples
-#' predicted_results <- predict(provoc_obj)
-predict.provoc <- function(provoc_obj,
+#' library(provoc)
+#' data("Baaijens")
+#' Baaijens$mutation <- parse_mutations(Baaijens$label)
+#' res <- provoc(formula = cbind(count, coverage) ~ B.1.1.7 + B.1.617.2, data = Baaijens)
+#' b1 <- Baaijens[Baaijens$sra == unique(Baaijens$sra)[1], ]
+#' b2 <- Baaijens[Baaijens$sra == unique(Baaijens$sra)[2], ]
+#' predicted_values <- predict.provoc(res, b1, b2)
+#' print(predicted_values)
+#' @export
+predict.provoc <- function(
+    provoc_obj,
+    b1, b2,
     newdata = NULL, type = NULL,
     dispersion = NULL, terms = NULL) {
-
     if (!"provoc" %in% class(provoc_obj)) {
         stop("Object must be of class 'provoc'")
     }
 
+    b1_index <- match(b1, provoc_obj$group)
+    b2_index <- match(b2, provoc_obj$group)
+
     proportions <- as.numeric(provoc_obj$rho)
-    variant_matrix <- get_varmat(provoc_obj)
+    variant_matrix <- attr(provoc_obj, "variant_matrix")
+
     if (any(!rownames(variant_matrix) %in% provoc_obj$variant)) {
         stop("Variant matrix does not match variants in results")
     }
 
-    results <- proportions %*% variant_matrix[provoc_obj$variant, ]
+    results <- proportions[b1_index] %*% variant_matrix[b2_index, ]
 
     return(results)
 }
diff --git a/man/find_mutation_column.Rd b/man/find_mutation_column.Rd
new file mode 100644
index 0000000..2a3e090
--- /dev/null
+++ b/man/find_mutation_column.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/provoc.R
+\name{find_mutation_column}
+\alias{find_mutation_column}
+\title{Finds the right mutation column in mutation_defs.}
+\usage{
+find_mutation_column(data, mutation_defs)
+}
+\arguments{
+\item{data}{Data frame containing count, coverage, and lineage columns}
+
+\item{mutation_defs}{Optional mutation definitions}
+}
+\value{
+A list of length 2 containing the possibly transposed mutation_defs and
+the right mutation column.
+}
+\description{
+Checks the columns of mutation_defs and if not found in columns will check the rows
+and transpose mutation_defs.
+}
diff --git a/man/parse_ivar.Rd b/man/parse_ivar.Rd
new file mode 100644
index 0000000..cabb349
--- /dev/null
+++ b/man/parse_ivar.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/parse_mutations.R
+\name{parse_ivar}
+\alias{parse_ivar}
+\title{Parse Mutations from the ivar pipeline}
+\usage{
+parse_ivar(ivar_output)
+}
+\arguments{
+\item{ivar_output}{A data frame imported from the ivar output. Must have columns POS and ALT}
+}
+\description{
+Parse Mutations from the ivar pipeline
+}
diff --git a/man/parse_one_ivar.Rd b/man/parse_one_ivar.Rd
new file mode 100644
index 0000000..4313460
--- /dev/null
+++ b/man/parse_one_ivar.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/parse_mutations.R
+\name{parse_one_ivar}
+\alias{parse_one_ivar}
+\title{Parse mutations from ivar pipeline}
+\usage{
+parse_one_ivar(pos, alt)
+}
+\arguments{
+\item{pos}{The position on the genome}
+
+\item{alt}{The alternate genotype}
+}
+\description{
+Parse mutations from ivar pipeline
+}
diff --git a/man/predict.provoc.Rd b/man/predict.provoc.Rd
index b502d47..dcf752d 100644
--- a/man/predict.provoc.Rd
+++ b/man/predict.provoc.Rd
@@ -6,6 +6,8 @@
 \usage{
 \method{predict}{provoc}(
   provoc_obj,
+  b1,
+  b2,
   newdata = NULL,
   type = NULL,
   dispersion = NULL,
@@ -15,6 +17,10 @@
 \arguments{
 \item{provoc_obj}{Named list with \code{proportions} and \code{variant_matrix}.}
 
+\item{b1}{Name of the first sra sample group.}
+
+\item{b2}{Name of the second sra sample group.}
+
 \item{newdata}{Not yet implemented.}
 
 \item{type}{Not yet implemented.}
@@ -36,5 +42,12 @@ associated variant matrix, performs matrix multiplication to
 predict outcomes, and returns results in the same order as the original data.
 }
 \examples{
-predicted_results <- predict(provoc_obj)
+library(provoc)
+data("Baaijens")
+Baaijens$mutation <- parse_mutations(Baaijens$label)
+res <- provoc(formula = cbind(count, coverage) ~ B.1.1.7 + B.1.617.2, data = Baaijens)
+b1 <- Baaijens[Baaijens$sra == unique(Baaijens$sra)[1], ]
+b2 <- Baaijens[Baaijens$sra == unique(Baaijens$sra)[2], ]
+predicted_values <- predict.provoc(res, b1, b2)
+print(predicted_values)
 }
diff --git a/man/provoc.Rd b/man/provoc.Rd
index d540fe6..595dd16 100644
--- a/man/provoc.Rd
+++ b/man/provoc.Rd
@@ -10,7 +10,7 @@ provoc(
   mutation_defs = NULL,
   by = NULL,
   update_interval = 20,
-  verbose = TRUE,
+  verbose = FALSE,
   annihilate = FALSE
 )
 }
diff --git a/man/variants_similarity.Rd b/man/variants_similarity.Rd
index 937247f..b9fdeea 100644
--- a/man/variants_similarity.Rd
+++ b/man/variants_similarity.Rd
@@ -9,7 +9,7 @@ variants_similarity(data, is_varmat)
 \arguments{
 \item{data}{A dataframe either before or after it has been fused with varmat}
 
-\item{is_varmat}{TRUE if data is a varaint matrix, FALSE if data is a fused dataframe}
+\item{is_varmat}{TRUE if data is a variant matrix, FALSE if data is a fused dataframe}
 }
 \value{
 none