diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index d45c136a..66bcb77c 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -44,9 +44,9 @@ jobs:
           JULIA_NUM_THREADS: ${{ matrix.num_threads }}
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v2
-
         with:
           files: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}
   docs:
     name: Documentation
     runs-on: ubuntu-latest
diff --git a/Project.toml b/Project.toml
index daae5911..3a962713 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "Pathfinder"
 uuid = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 authors = ["Seth Axen <seth.axen@gmail.com> and contributors"]
-version = "0.7.6"
+version = "0.7.7-DEV"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -36,7 +36,7 @@ MCMCChains = "5"
 Optim = "1.4"
 Optimization = "3"
 OptimizationOptimJL = "0.1"
-PDMats = "0.11"
+PDMats = "0.11.26"
 PSIS = "0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9"
 ProgressLogging = "0.1.4"
 Requires = "1"
diff --git a/src/woodbury.jl b/src/woodbury.jl
index 4937afd3..8306f17e 100644
--- a/src/woodbury.jl
+++ b/src/woodbury.jl
@@ -270,6 +270,18 @@ function WoodburyPDMat(A, B, D)
     )
 end
 
+Base.convert(::Type{WoodburyPDMat{T}}, a::WoodburyPDMat{T}) where {T<:Real} = a
+function Base.convert(::Type{WoodburyPDMat{T}}, a::WoodburyPDMat) where {T<:Real}
+    A = convert(AbstractMatrix{T}, a.A)
+    B = convert(AbstractMatrix{T}, a.B)
+    D = convert(AbstractMatrix{T}, a.D)
+    F = pdfactorize(A, B, D)
+    return WoodburyPDMat{T,typeof(A),typeof(B),typeof(D),typeof(F)}(A, B, D, F)
+end
+function Base.convert(::Type{PDMats.AbstractPDMat{T}}, a::WoodburyPDMat) where {T<:Real}
+    return convert(WoodburyPDMat{T}, a)
+end
+
 pdfactorize(A::WoodburyPDMat) = A.F
 
 LinearAlgebra.factorize(A::WoodburyPDMat) = pdfactorize(A)
@@ -354,8 +366,13 @@ end
 
 PDMats.dim(W::WoodburyPDMat) = size(W.A, 1)
 
-function PDMats.invquad(W::WoodburyPDMat, x::AbstractVector{T}) where {T}
-    return sum(abs2, pdfactorize(W).L \ x)
+function PDMats.invquad(W::WoodburyPDMat, x::AbstractVecOrMat{T}) where {T}
+    WL_inv_x = pdfactorize(W).L \ x
+    if x isa AbstractVector
+        return sum(abs2, WL_inv_x)
+    else
+        return vec(sum(abs2, WL_inv_x; dims=1))
+    end
 end
 
 function PDMats.invquad!(r::AbstractArray, W::WoodburyPDMat, x::AbstractMatrix{T}) where {T}
@@ -370,11 +387,17 @@ function PDMats.quad!(r::AbstractArray, W::WoodburyPDMat, x::AbstractMatrix{T})
     return r
 end
 
-function PDMats.quad(W::WoodburyPDMat, x::AbstractVector{T}) where {T}
-    v = pdfactorize(W).R * x
-    return sum(abs2, v)
+function PDMats.quad(W::WoodburyPDMat, x::AbstractVecOrMat{T}) where {T}
+    WR_inv_x = pdfactorize(W).R * x
+    if x isa AbstractVector
+        return sum(abs2, WR_inv_x)
+    else
+        return vec(sum(abs2, WR_inv_x; dims=1))
+    end
 end
 
+PDMats.unwhiten(W::WoodburyPDMat, x::AbstractVecOrMat) = pdfactorize(W).L * x
+
 function PDMats.unwhiten!(
     r::AbstractVecOrMat{T}, W::WoodburyPDMat, x::AbstractVecOrMat{T}
 ) where {T}
@@ -382,6 +405,16 @@ function PDMats.unwhiten!(
     return lmul!(pdfactorize(W).L, r)
 end
 
+PDMats.whiten(W::WoodburyPDMat, x::AbstractVecOrMat) = pdfactorize(W).L \ x
+
+function PDMats.whiten!(
+    r::AbstractVecOrMat{T}, W::WoodburyPDMat, x::AbstractVecOrMat{T}
+) where {T}
+    copyto!(r, x)
+    return ldiv!(pdfactorize(W).L, r)
+end
+
+# similar to unwhiten!(r, inv(W), x) but without the inversion
 function invunwhiten!(
     r::AbstractVecOrMat{T}, W::WoodburyPDMat, x::AbstractVecOrMat{T}
 ) where {T}
diff --git a/test/woodbury.jl b/test/woodbury.jl
index 56d9d9df..ec0f80bf 100644
--- a/test/woodbury.jl
+++ b/test/woodbury.jl
@@ -82,6 +82,10 @@ test_factorization(W::WoodburyPDMat) = test_factorization(W.A, W.B, W.D, W.F)
             Fmat = Rmat' * Rmat
             F = @inferred WoodburyPDFactorization(U, Q, V)
             @test F isa LinearAlgebra.Factorization{T}
+            @test size(F) == (n, n)
+            @test size(F, 1) == n
+            @test size(F, 2) == n
+            @test size(F, 3) == 1
             @test transpose(F) === F
             @test adjoint(F) === F
             @test propertynames(F) == (:L, :R)
@@ -175,12 +179,27 @@ end
             @test W ≈ Wmat
             @test WoodburyPDMat(A, B, big.(D)) isa WoodburyPDMat{BigFloat}
             @test Matrix(WoodburyPDMat(A, B, big.(D))) ≈ Wmat
+
             Wbig = convert(AbstractMatrix{BigFloat}, W)
             @test Wbig isa WoodburyPDMat{BigFloat}
             @test Wbig ≈ Wmat
             @test convert(AbstractMatrix{T}, W) === W
             @test W.F isa WoodburyPDFactorization
             @test Matrix(W.F) ≈ Wmat
+
+            @test convert(PDMats.AbstractPDMat{T}, W) === W
+            Wbig2 = convert(PDMats.AbstractPDMat{BigFloat}, W)
+            @test Wbig2 isa WoodburyPDMat{BigFloat}
+            @test Wbig2 == Wbig
+
+            @test convert(WoodburyPDMat{T}, W) === W
+            @test convert(WoodburyPDMat{BigFloat}, W) == Wbig
+
+            @test AbstractMatrix{T}(W) isa WoodburyPDMat{T}
+            @test AbstractMatrix{T}(W) == W
+            @test AbstractMatrix{BigFloat}(W) isa WoodburyPDMat{BigFloat}
+            @test AbstractMatrix{BigFloat}(W) == Wbig
+
             test_factorization(W)
         end
 
@@ -291,14 +310,50 @@ end
             @test PDMats.dim(W) == n
         end
 
-        @testset "unwhiten" begin
+        @testset "whiten/whiten!" begin
             L, _ = factorize(W)
 
             x = randn(T, n)
-            @test @inferred(unwhiten(W, x)) ≈ L * x
+            z = @inferred whiten(W, x)
+            @test size(x) == size(x)
+            @test dot(z, z) ≈ dot(x, invWmat, x)
+            z2 = similar(z)
+            @test whiten!(z2, W, x) === z2
+            @test z2 ≈ z
+
+            X = randn(T, n, 10)
+            Z = @inferred whiten(W, X)
+            @test size(Z) == size(X)
+            for (x, z) in zip(eachcol(X), eachcol(Z))
+                @test dot(z, z) ≈ dot(x, invWmat, x)
+            end
+            Z2 = similar(Z)
+            @test whiten!(Z2, W, X) === Z2
+            @test Z2 ≈ Z
+        end
 
-            X = randn(T, n, 100)
-            @test @inferred(unwhiten(W, X)) ≈ L * X
+        @testset "unwhiten/unwhiten!" begin
+            L, _ = factorize(W)
+
+            z = randn(T, n)
+            x = @inferred unwhiten(W, z)
+            @test size(x) == size(z)
+            @test dot(x, invWmat, x) ≈ dot(z, z)
+            @test whiten(W, x) ≈ z
+            x2 = similar(x)
+            @test unwhiten!(x2, W, z) === x2
+            @test x2 ≈ x
+
+            Z = randn(T, n, 10)
+            X = @inferred unwhiten(W, Z)
+            @test whiten(W, X) ≈ Z
+            @test size(X) == size(Z)
+            for (x, z) in zip(eachcol(X), eachcol(Z))
+                @test dot(x, invWmat, x) ≈ dot(z, z)
+            end
+            X2 = similar(X)
+            @test unwhiten!(X2, W, Z) === X2
+            @test X2 ≈ X
         end
 
         @testset "invunwhiten!" begin
@@ -311,32 +366,40 @@ end
             @test @inferred(Pathfinder.invunwhiten!(similar(X), W, X)) ≈ R \ X
         end
 
-        @testset "PDMats.quad" begin
+        @testset "quad/quad!" begin
             x = randn(T, n)
             @test @inferred(quad(W, x)) ≈ dot(x, Wmat, x)
 
             u = randn(T, n)
             @test quad(W, Pathfinder.invunwhiten!(similar(u), W, u)) ≈ dot(u, u)
 
-            X = randn(T, n, 100)
-            @test @inferred(quad(W, X)) ≈ quad(PDMats.PDMat(Symmetric(Wmat)), X)
+            X = randn(T, n, 10)
+            quad_W_X = @inferred quad(W, X)
+            @test quad_W_X ≈ quad(PDMats.PDMat(Symmetric(Wmat)), X)
+            quad_W_X2 = similar(quad_W_X)
+            @test quad!(quad_W_X2, W, X) === quad_W_X2
+            @test quad_W_X2 ≈ quad_W_X
 
-            U = randn(T, n, 100)
+            U = randn(T, n, 10)
             @test quad(W, Pathfinder.invunwhiten!(similar(U), W, U)) ≈
                 vec(sum(abs2, U; dims=1))
         end
 
-        @testset "PDMats.invquad" begin
+        @testset "invquad/invquad!" begin
             x = randn(T, n)
             @test @inferred(invquad(W, x)) ≈ dot(x, inv(Wmat), x)
 
             u = randn(T, n)
             @test invquad(W, unwhiten(W, u)) ≈ dot(u, u)
 
-            X = randn(T, n, 100)
-            @test @inferred(invquad(W, X)) ≈ invquad(PDMats.PDMat(Symmetric(Wmat)), X)
+            X = randn(T, n, 10)
+            quad_invW_X = @inferred invquad(W, X)
+            @test quad_invW_X ≈ invquad(PDMats.PDMat(Symmetric(Wmat)), X)
+            quad_invW_X2 = similar(quad_invW_X)
+            @test invquad!(quad_invW_X2, W, X) === quad_invW_X2
+            @test quad_invW_X2 ≈ quad_invW_X
 
-            U = randn(T, n, 100)
+            U = randn(T, n, 10)
             @test invquad(W, unwhiten(W, U)) ≈ vec(sum(abs2, U; dims=1))
         end
     end