diff --git a/Project.toml b/Project.toml
index b31e817..42f0260 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "PythonOT"
 uuid = "3c485715-4278-42b2-9b5f-8f00e43c12ef"
 authors = ["David Widmann"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
diff --git a/docs/src/api.md b/docs/src/api.md
index 157af1d..feaa2d7 100644
--- a/docs/src/api.md
+++ b/docs/src/api.md
@@ -5,6 +5,8 @@
 ```@docs
 emd
 emd2
+emd_1d
+emd2_1d
 ```
 
 ## Regularized optimal transport
diff --git a/src/PythonOT.jl b/src/PythonOT.jl
index b1cd684..9b3c534 100644
--- a/src/PythonOT.jl
+++ b/src/PythonOT.jl
@@ -4,6 +4,8 @@ using PyCall: PyCall
 
 export emd,
     emd2,
+    emd_1d,
+    emd2_1d,
     sinkhorn,
     sinkhorn2,
     barycenter,
diff --git a/src/lib.jl b/src/lib.jl
index da10c79..219820f 100644
--- a/src/lib.jl
+++ b/src/lib.jl
@@ -69,6 +69,78 @@ function emd2(μ, ν, C; kwargs...)
     return pot.lp.emd2(μ, ν, PyCall.PyReverseDims(permutedims(C)); kwargs...)
 end
 
+"""
+    emd_1d(xsource, xtarget; kwargs...)
+
+Compute the optimal transport plan for the Monge-Kantorovich problem with univariate
+discrete measures with support `xsource` and `xtarget` as source and target marginals.
+
+This function is a wrapper of the function
+[`emd_1d`](https://pythonot.github.io/all.html#ot.emd_1d) in the Python Optimal Transport
+package. Keyword arguments are listed in the documentation of the Python function.
+
+# Examples
+
+```jldoctest
+julia> xsource = [0.2, 0.5];
+
+julia> xtarget = [0.8, 0.3];
+
+julia> emd_1d(xsource, xtarget)
+2×2 Matrix{Float64}:
+ 0.0  0.5
+ 0.5  0.0
+
+julia> histogram_source = [0.8, 0.2];
+
+julia> histogram_target = [0.7, 0.3];
+
+julia> emd_1d(xsource, xtarget; a=histogram_source, b=histogram_target)
+2×2 Matrix{Float64}:
+ 0.5  0.3
+ 0.2  0.0
+```
+
+See also: [`emd`](@ref), [`emd2_1d`](@ref)
+"""
+function emd_1d(xsource, xtarget; kwargs...)
+    return pot.lp.emd_1d(xsource, xtarget; kwargs...)
+end
+
+"""
+    emd2_1d(xsource, xtarget; kwargs...)
+
+Compute the optimal transport cost for the Monge-Kantorovich problem with univariate
+discrete measures with support `xsource` and `xtarget` as source and target marginals.
+
+This function is a wrapper of the function
+[`emd2_1d`](https://pythonot.github.io/all.html#ot.emd2_1d) in the Python Optimal Transport
+package. Keyword arguments are listed in the documentation of the Python function.
+
+# Examples
+
+```jldoctest
+julia> xsource = [0.2, 0.5];
+
+julia> xtarget = [0.8, 0.3];
+
+julia> round(emd2_1d(xsource, xtarget); sigdigits=6)
+0.05
+
+julia> histogram_source = [0.8, 0.2];
+
+julia> histogram_target = [0.7, 0.3];
+
+julia> round(emd2_1d(xsource, xtarget; a=histogram_source, b=histogram_target); sigdigits=6)
+0.201
+```
+
+See also: [`emd2`](@ref), [`emd2_1d`](@ref)
+"""
+function emd2_1d(xsource, xtarget; kwargs...)
+    return pot.lp.emd2_1d(xsource, xtarget; kwargs...)
+end
+
 """
     sinkhorn(μ, ν, C, ε; kwargs...)