diff --git a/NEWS.md b/NEWS.md
index 2bb746dcff3333..34c29729a0cce8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -80,9 +80,9 @@ Library improvements
   * Added `unique!` which is an inplace version of `unique` ([#20549]).
 
   * Uses of `Val{c}` in `Base` has been replaced with `Val{c}()`, which is now easily
-    accessible via the `@pure` constructor `Val(c)`. Functions are defined as 
-    `f(::Val{c}) = ...` and called by `f(Val(c))`. Notable affected function are: 
-    `ntuple`, `fill_to_length`, `Base.literal_pow`.
+    accessible via the `@pure` constructor `Val(c)`. Functions are defined as
+    `f(::Val{c}) = ...` and called by `f(Val(c))`. Notable affected functions include:
+    `ntuple`, `Base.literal_pow`, `sqrtm`, `lufact`, `lufact!`, `qrfact`, `qrfact!`.
 
 Compiler/Runtime improvements
 -----------------------------
diff --git a/base/deprecated.jl b/base/deprecated.jl
index 46ec2d6724f841..e84dbefe848fad 100644
--- a/base/deprecated.jl
+++ b/base/deprecated.jl
@@ -1490,6 +1490,10 @@ export conv, conv2, deconv, filt, filt!, xcorr
 @deprecate literal_pow{N}(a,b , ::Type{Val{N}}) literal_pow(f, Val(N))
 #@deprecate IteratorsMD.split(t, V::Type{Val{n}}) IteratorsMD.split(t, Val(n))
 @deprecate sqrtm{T,realmatrix}(A::UpperTriangular{T},::Type{Val{realmatrix}}) sqrtm(A, Val(realmatrix))
+@deprecate lufact(A, pivot::Union{Type{Val{false}}, Type{Val{true}}}) lufact(A, pivot)
+@deprecate lufact!(A, pivot::Union{Type{Val{false}}, Type{Val{true}}}) lufact!(A, pivot)
+@deprecate qrfact(A, pivot::Union{Type{Val{false}}, Type{Val{true}}}) qrfact(A, pivot)
+@deprecate qrfact!(A, pivot::Union{Type{Val{false}}, Type{Val{true}}}) qrfact!(A, pivot)
 
 # END 0.7 deprecations
 
diff --git a/base/linalg/lu.jl b/base/linalg/lu.jl
index 8d2558b9cb9a22..7293abb78d4941 100644
--- a/base/linalg/lu.jl
+++ b/base/linalg/lu.jl
@@ -12,8 +12,8 @@ end
 LU(factors::AbstractMatrix{T}, ipiv::Vector{BlasInt}, info::BlasInt) where {T} = LU{T,typeof(factors)}(factors, ipiv, info)
 
 # StridedMatrix
-function lufact!(A::StridedMatrix{T}, pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true}) where T<:BlasFloat
-    if pivot === Val{false}
+function lufact!(A::StridedMatrix{T}, pivot::Union{Val{false}, Val{true}} = Val(true)) where T<:BlasFloat
+    if pivot === Val(false)
         return generic_lufact!(A, pivot)
     end
     lpt = LAPACK.getrf!(A)
@@ -21,15 +21,15 @@ function lufact!(A::StridedMatrix{T}, pivot::Union{Type{Val{false}}, Type{Val{tr
 end
 
 """
-    lufact!(A, pivot=Val{true}) -> LU
+    lufact!(A, pivot=Val(true)) -> LU
 
 `lufact!` is the same as [`lufact`](@ref), but saves space by overwriting the
 input `A`, instead of creating a copy. An [`InexactError`](@ref)
 exception is thrown if the factorization produces a number not representable by the
 element type of `A`, e.g. for integer types.
 """
-lufact!(A::StridedMatrix, pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true}) = generic_lufact!(A, pivot)
-function generic_lufact!(A::StridedMatrix{T}, ::Type{Val{Pivot}} = Val{true}) where {T,Pivot}
+lufact!(A::StridedMatrix, pivot::Union{Val{false}, Val{true}} = Val(true)) = generic_lufact!(A, pivot)
+function generic_lufact!(A::StridedMatrix{T}, ::Val{Pivot} = Val(true)) where {T,Pivot}
     m, n = size(A)
     minmn = min(m,n)
     info = 0
@@ -79,12 +79,12 @@ end
 
 # floating point types doesn't have to be promoted for LU, but should default to pivoting
 lufact(A::Union{AbstractMatrix{T}, AbstractMatrix{Complex{T}}},
-    pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true}) where {T<:AbstractFloat} =
+    pivot::Union{Val{false}, Val{true}} = Val(true)) where {T<:AbstractFloat} =
         lufact!(copy(A), pivot)
 
 # for all other types we must promote to a type which is stable under division
 """
-    lufact(A [,pivot=Val{true}]) -> F::LU
+    lufact(A [,pivot=Val(true)]) -> F::LU
 
 Compute the LU factorization of `A`.
 
@@ -135,7 +135,7 @@ julia> F[:L] * F[:U] == A[F[:p], :]
 true
 ```
 """
-function lufact(A::AbstractMatrix{T}, pivot::Union{Type{Val{false}}, Type{Val{true}}}) where T
+function lufact(A::AbstractMatrix{T}, pivot::Union{Val{false}, Val{true}}) where T
     S = typeof(zero(T)/one(T))
     AA = similar(A, S, size(A))
     copy!(AA, A)
@@ -146,13 +146,13 @@ function lufact(A::AbstractMatrix{T}) where T
     S = typeof(zero(T)/one(T))
     AA = similar(A, S, size(A))
     copy!(AA, A)
-    F = lufact!(AA, Val{false})
+    F = lufact!(AA, Val(false))
     if F.info == 0
         return F
     else
         AA = similar(A, S, size(A))
         copy!(AA, A)
-        return lufact!(AA, Val{true})
+        return lufact!(AA, Val(true))
     end
 end
 
@@ -162,11 +162,11 @@ lufact(F::LU) = F
 lu(x::Number) = (one(x), x, 1)
 
 """
-    lu(A, pivot=Val{true}) -> L, U, p
+    lu(A, pivot=Val(true)) -> L, U, p
 
 Compute the LU factorization of `A`, such that `A[p,:] = L*U`.
 By default, pivoting is used. This can be overridden by passing
-`Val{false}` for the second argument.
+`Val(false)` for the second argument.
 
 See also [`lufact`](@ref).
 
@@ -185,7 +185,7 @@ julia> A[p, :] == L * U
 true
 ```
 """
-function lu(A::AbstractMatrix, pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true})
+function lu(A::AbstractMatrix, pivot::Union{Val{false}, Val{true}} = Val(true))
     F = lufact(A, pivot)
     F[:L], F[:U], F[:p]
 end
@@ -319,7 +319,7 @@ end
 # Tridiagonal
 
 # See dgttrf.f
-function lufact!(A::Tridiagonal{T}, pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true}) where T
+function lufact!(A::Tridiagonal{T}, pivot::Union{Val{false}, Val{true}} = Val(true)) where T
     n = size(A, 1)
     info = 0
     ipiv = Vector{BlasInt}(n)
@@ -334,7 +334,7 @@ function lufact!(A::Tridiagonal{T}, pivot::Union{Type{Val{false}}, Type{Val{true
         end
         for i = 1:n-2
             # pivot or not?
-            if pivot === Val{false} || abs(d[i]) >= abs(dl[i])
+            if pivot === Val(false) || abs(d[i]) >= abs(dl[i])
                 # No interchange
                 if d[i] != 0
                     fact = dl[i]/d[i]
@@ -357,7 +357,7 @@ function lufact!(A::Tridiagonal{T}, pivot::Union{Type{Val{false}}, Type{Val{true
         end
         if n > 1
             i = n-1
-            if pivot === Val{false} || abs(d[i]) >= abs(dl[i])
+            if pivot === Val(false) || abs(d[i]) >= abs(dl[i])
                 if d[i] != 0
                     fact = dl[i]/d[i]
                     dl[i] = fact
diff --git a/base/linalg/qr.jl b/base/linalg/qr.jl
index ca380455e4c843..6cdd2d0dd3ede7 100644
--- a/base/linalg/qr.jl
+++ b/base/linalg/qr.jl
@@ -194,26 +194,26 @@ function qrfactPivotedUnblocked!(A::StridedMatrix)
 end
 
 # LAPACK version
-qrfact!(A::StridedMatrix{<:BlasFloat}, ::Type{Val{false}}) = QRCompactWY(LAPACK.geqrt!(A, min(minimum(size(A)), 36))...)
-qrfact!(A::StridedMatrix{<:BlasFloat}, ::Type{Val{true}}) = QRPivoted(LAPACK.geqp3!(A)...)
-qrfact!(A::StridedMatrix{<:BlasFloat}) = qrfact!(A, Val{false})
+qrfact!(A::StridedMatrix{<:BlasFloat}, ::Val{false}) = QRCompactWY(LAPACK.geqrt!(A, min(minimum(size(A)), 36))...)
+qrfact!(A::StridedMatrix{<:BlasFloat}, ::Val{true}) = QRPivoted(LAPACK.geqp3!(A)...)
+qrfact!(A::StridedMatrix{<:BlasFloat}) = qrfact!(A, Val(false))
 
 # Generic fallbacks
 
 """
-    qrfact!(A, pivot=Val{false})
+    qrfact!(A, pivot=Val(false))
 
 `qrfact!` is the same as [`qrfact`](@ref) when `A` is a subtype of
 `StridedMatrix`, but saves space by overwriting the input `A`, instead of creating a copy.
 An [`InexactError`](@ref) exception is thrown if the factorization produces a number not
 representable by the element type of `A`, e.g. for integer types.
 """
-qrfact!(A::StridedMatrix, ::Type{Val{false}}) = qrfactUnblocked!(A)
-qrfact!(A::StridedMatrix, ::Type{Val{true}}) = qrfactPivotedUnblocked!(A)
-qrfact!(A::StridedMatrix) = qrfact!(A, Val{false})
+qrfact!(A::StridedMatrix, ::Val{false}) = qrfactUnblocked!(A)
+qrfact!(A::StridedMatrix, ::Val{true}) = qrfactPivotedUnblocked!(A)
+qrfact!(A::StridedMatrix) = qrfact!(A, Val(false))
 
 """
-    qrfact(A, pivot=Val{false}) -> F
+    qrfact(A, pivot=Val(false)) -> F
 
 Compute the QR factorization of the matrix `A`: an orthogonal (or unitary if `A` is
 complex-valued) matrix `Q`, and an upper triangular matrix `R` such that
@@ -224,7 +224,7 @@ A = Q R
 
 The returned object `F` stores the factorization in a packed format:
 
- - if `pivot == Val{true}` then `F` is a [`QRPivoted`](@ref) object,
+ - if `pivot == Val(true)` then `F` is a [`QRPivoted`](@ref) object,
 
  - otherwise if the element type of `A` is a BLAS type ([`Float32`](@ref), [`Float64`](@ref),
    `Complex64` or `Complex128`), then `F` is a [`QRCompactWY`](@ref) object,
@@ -283,21 +283,21 @@ end
 qrfact(x::Number) = qrfact(fill(x,1,1))
 
 """
-    qr(A, pivot=Val{false}; thin::Bool=true) -> Q, R, [p]
+    qr(A, pivot=Val(false); thin::Bool=true) -> Q, R, [p]
 
 Compute the (pivoted) QR factorization of `A` such that either `A = Q*R` or `A[:,p] = Q*R`.
 Also see [`qrfact`](@ref).
 The default is to compute a thin factorization. Note that `R` is not
 extended with zeros when the full `Q` is requested.
 """
-qr(A::Union{Number, AbstractMatrix}, pivot::Union{Type{Val{false}}, Type{Val{true}}}=Val{false}; thin::Bool=true) =
+qr(A::Union{Number, AbstractMatrix}, pivot::Union{Val{false}, Val{true}}=Val(false); thin::Bool=true) =
     _qr(A, pivot, thin=thin)
-function _qr(A::Union{Number, AbstractMatrix}, ::Type{Val{false}}; thin::Bool=true)
-    F = qrfact(A, Val{false})
+function _qr(A::Union{Number, AbstractMatrix}, ::Val{false}; thin::Bool=true)
+    F = qrfact(A, Val(false))
     full(getq(F), thin=thin), F[:R]::Matrix{eltype(F)}
 end
-function _qr(A::Union{Number, AbstractMatrix}, ::Type{Val{true}}; thin::Bool=true)
-    F = qrfact(A, Val{true})
+function _qr(A::Union{Number, AbstractMatrix}, ::Val{true}; thin::Bool=true)
+    F = qrfact(A, Val(true))
     full(getq(F), thin=thin), F[:R]::Matrix{eltype(F)}, F[:p]::Vector{BlasInt}
 end
 
diff --git a/base/sparse/spqr.jl b/base/sparse/spqr.jl
index c7a88f7a1ea7ce..a507c8cdcbf22d 100644
--- a/base/sparse/spqr.jl
+++ b/base/sparse/spqr.jl
@@ -137,7 +137,7 @@ function qmult(method::Integer, QR::Factorization{Tv}, X::Dense{Tv}) where Tv<:V
 end
 
 
-qrfact(A::SparseMatrixCSC, ::Type{Val{true}}) = factorize(ORDERING_DEFAULT, DEFAULT_TOL, Sparse(A, 0))
+qrfact(A::SparseMatrixCSC, ::Val{true}) = factorize(ORDERING_DEFAULT, DEFAULT_TOL, Sparse(A, 0))
 
 """
     qrfact(A) -> SPQR.Factorization
@@ -147,7 +147,7 @@ The main application of this type is to solve least squares problems with [`\\`]
 calls the C library SPQR and a few additional functions from the library are wrapped but not
 exported.
 """
-qrfact(A::SparseMatrixCSC) = qrfact(A, Val{true})
+qrfact(A::SparseMatrixCSC) = qrfact(A, Val(true))
 
 # With a real lhs and complex rhs with the same precision, we can reinterpret
 # the complex rhs as a real rhs with twice the number of columns
diff --git a/test/linalg/cholesky.jl b/test/linalg/cholesky.jl
index 3152ca219475d2..59a96d5c27514d 100644
--- a/test/linalg/cholesky.jl
+++ b/test/linalg/cholesky.jl
@@ -132,9 +132,9 @@ using Base.LinAlg: BlasComplex, BlasFloat, BlasReal, QRPivoted, PosDefException
 
         #pivoted upper Cholesky
         if eltya != BigFloat
-            cz = cholfact(Hermitian(zeros(eltya,n,n)), Val{true})
+            cz = cholfact(Hermitian(zeros(eltya,n,n)), Val(true))
             @test_throws Base.LinAlg.RankDeficientException Base.LinAlg.chkfullrank(cz)
-            cpapd = cholfact(apdh, Val{true})
+            cpapd = cholfact(apdh, Val(true))
             @test rank(cpapd) == n
             @test all(diff(diag(real(cpapd.factors))).<=0.) # diagonal should be non-increasing
             if isreal(apd)
@@ -175,11 +175,11 @@ using Base.LinAlg: BlasComplex, BlasFloat, BlasReal, QRPivoted, PosDefException
 
                 if eltya != BigFloat && eltyb != BigFloat # Note! Need to implement pivoted Cholesky decomposition in julia
 
-                    cpapd = cholfact(apdh, Val{true})
+                    cpapd = cholfact(apdh, Val(true))
                     @test norm(apd * (cpapd\b) - b)/norm(b) <= ε*κ*n # Ad hoc, revisit
                     @test norm(apd * (cpapd\b[1:n]) - b[1:n])/norm(b[1:n]) <= ε*κ*n
 
-                    lpapd = cholfact(apdhL, Val{true})
+                    lpapd = cholfact(apdhL, Val(true))
                     @test norm(apd * (lpapd\b) - b)/norm(b) <= ε*κ*n # Ad hoc, revisit
                     @test norm(apd * (lpapd\b[1:n]) - b[1:n])/norm(b[1:n]) <= ε*κ*n
 
@@ -251,7 +251,7 @@ end
         0.25336108035924787 + 0.975317836492159im 0.0628393808469436 - 0.1253397353973715im
         0.11192755545114 - 0.1603741874112385im 0.8439562576196216 + 1.0850814110398734im
         -1.0568488936791578 - 0.06025820467086475im 0.12696236014017806 - 0.09853584666755086im]
-    cholfact(Hermitian(apd, :L), Val{true}) \ b
+    cholfact(Hermitian(apd, :L), Val(true)) \ b
     r = factorize(apd)[:U]
     E = abs.(apd - r'*r)
     ε = eps(abs(float(one(Complex64))))
@@ -273,7 +273,7 @@ end
 end
 
 @testset "fail for non-BLAS element types" begin
-    @test_throws ArgumentError cholfact!(Hermitian(rand(Float16, 5,5)), Val{true})
+    @test_throws ArgumentError cholfact!(Hermitian(rand(Float16, 5,5)), Val(true))
 end
 
 @testset "throw for non positive definite matrix" begin
diff --git a/test/linalg/generic.jl b/test/linalg/generic.jl
index b4a8017f9d9453..830b48105b0b6f 100644
--- a/test/linalg/generic.jl
+++ b/test/linalg/generic.jl
@@ -335,10 +335,10 @@ Base.transpose(a::ModInt{n}) where {n} = a  # see Issue 20978
 A = [ModInt{2}(1) ModInt{2}(0); ModInt{2}(1) ModInt{2}(1)]
 b = [ModInt{2}(1), ModInt{2}(0)]
 
-@test A*(lufact(A, Val{false})\b) == b
+@test A*(lufact(A, Val(false))\b) == b
 
 # Needed for pivoting:
 Base.abs(a::ModInt{n}) where {n} = a
 Base.:<(a::ModInt{n}, b::ModInt{n}) where {n} = a.k < b.k
 
-@test A*(lufact(A, Val{true})\b) == b
+@test A*(lufact(A, Val(true))\b) == b
diff --git a/test/linalg/qr.jl b/test/linalg/qr.jl
index 2de9f65fa774ce..84fe7462aaa013 100644
--- a/test/linalg/qr.jl
+++ b/test/linalg/qr.jl
@@ -65,8 +65,8 @@ debug && println("QR decomposition (without pivoting)")
                 @test sprint(show,qra) == "$(typeof(qra)) with factors Q and R:\n$qstring\n$rstring"
 
 debug && println("Thin QR decomposition (without pivoting)")
-                qra   = @inferred qrfact(a[:,1:n1], Val{false})
-                @inferred qr(a[:,1:n1], Val{false})
+                qra   = @inferred qrfact(a[:,1:n1], Val(false))
+                @inferred qr(a[:,1:n1], Val(false))
                 q,r   = qra[:Q], qra[:R]
                 @test_throws KeyError qra[:Z]
                 @test q'*full(q, thin=false) ≈ eye(n)
@@ -82,8 +82,8 @@ debug && println("Thin QR decomposition (without pivoting)")
                 end
 
 debug && println("(Automatic) Fat (pivoted) QR decomposition")
-                @inferred qrfact(a, Val{true})
-                @inferred qr(a, Val{true})
+                @inferred qrfact(a, Val(true))
+                @inferred qr(a, Val(true))
 
                 qrpa  = factorize(a[1:n1,:])
                 q,r = qrpa[:Q], qrpa[:R]
@@ -134,7 +134,7 @@ debug && println("Matmul with QR factorizations")
             @test_throws DimensionMismatch Base.LinAlg.A_mul_B!(q,zeros(eltya,n1+1))
             @test_throws DimensionMismatch Base.LinAlg.Ac_mul_B!(q,zeros(eltya,n1+1))
 
-            qra = qrfact(a[:,1:n1], Val{false})
+            qra = qrfact(a[:,1:n1], Val(false))
             q, r = qra[:Q], qra[:R]
             @test A_mul_B!(full(q, thin=false)',q) ≈ eye(n)
             @test_throws DimensionMismatch A_mul_B!(eye(eltya,n+1),q)
@@ -149,8 +149,8 @@ end
 # Because transpose(x) == x
 @test_throws ErrorException transpose(qrfact(randn(3,3)))
 @test_throws ErrorException ctranspose(qrfact(randn(3,3)))
-@test_throws ErrorException transpose(qrfact(randn(3,3), Val{false}))
-@test_throws ErrorException ctranspose(qrfact(randn(3,3), Val{false}))
+@test_throws ErrorException transpose(qrfact(randn(3,3), Val(false)))
+@test_throws ErrorException ctranspose(qrfact(randn(3,3), Val(false)))
 @test_throws ErrorException transpose(qrfact(big.(randn(3,3))))
 @test_throws ErrorException ctranspose(qrfact(big.(randn(3,3))))