diff --git a/src/curve_fit.jl b/src/curve_fit.jl
index 23794d8..b795864 100755
--- a/src/curve_fit.jl
+++ b/src/curve_fit.jl
@@ -15,16 +15,7 @@ StatsBase.weights(lfr::LsqFitResult) = lfr.wt
 StatsBase.residuals(lfr::LsqFitResult) = lfr.resid
 mse(lfr::LsqFitResult) = rss(lfr)/dof(lfr)
 
-function check_data_health(xdata, ydata)
-    if any(ismissing, xdata) || any(ismissing, ydata)
-        error("Data contains `missing` values and a fit cannot be performed")
-    end
-    if any(isinf, xdata) || any(isinf, ydata) || any(isnan, xdata) || any(isnan, ydata)
-        error("Data contains `Inf` or `NaN` values and a fit cannot be performed")
-    end
-end
-
-function check_data_health(xdata, ydata, wt)
+function check_data_health(xdata, ydata, wt = [])
     if any(ismissing, xdata) || any(ismissing, ydata) || any(ismissing, wt)
         error("Data contains `missing` values and a fit cannot be performed")
     end
@@ -146,7 +137,7 @@ function curve_fit(model, jacobian_model,
 end
 
 function curve_fit(model, xdata::AbstractArray, ydata::AbstractArray, wt::AbstractArray, p0::AbstractArray; inplace = false, kwargs...)
-    check_data_health(xdata, ydata)
+    check_data_health(xdata, ydata, wt)
     # construct a weighted cost function, with a vector weight for each ydata
     # for example, this might be wt = 1/sigma where sigma is some error term
     u = sqrt.(wt) # to be consistant with the matrix form
@@ -162,7 +153,7 @@ end
 
 function curve_fit(model, jacobian_model,
             xdata::AbstractArray, ydata::AbstractArray, wt::AbstractArray, p0::AbstractArray; inplace = false, kwargs...)
-    check_data_health(xdata, ydata)
+    check_data_health(xdata, ydata, wt)
     u = sqrt.(wt) # to be consistant with the matrix form
 
     if inplace
@@ -177,7 +168,7 @@ function curve_fit(model, jacobian_model,
 end
 
 function curve_fit(model, xdata::AbstractArray, ydata::AbstractArray, wt::AbstractMatrix, p0::AbstractArray; kwargs...)
-    check_data_health(xdata, ydata)
+    check_data_health(xdata, ydata, wt)
 
     # as before, construct a weighted cost function with where this
     # method uses a matrix weight.
@@ -194,7 +185,7 @@ end
 
 function curve_fit(model, jacobian_model,
             xdata::AbstractArray, ydata::AbstractArray, wt::AbstractMatrix, p0::AbstractArray; kwargs...)
-    check_data_health(xdata, ydata)
+    check_data_health(xdata, ydata, wt)
 
     u = cholesky(wt).U