diff --git a/.travis.yml b/.travis.yml
index 0197a78b..d1a1cd10 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,18 +3,19 @@ os:
   - linux
   - osx
 julia:
-  - 0.7
-  - 1.0
+  - 1.2
+  - 1.3
   - nightly
 notifications:
   email: false
 # Work around a Travis bug
 git:
   depth: 999999
-# Uncomment the following lines to override the default test script
-#script:
-#  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
-#  - julia -e 'Pkg.clone(pwd()); Pkg.build("StatsBase"); Pkg.test("StatsBase"; coverage=true)'
+script:
+  - if [[ -a .git/shallow ]]; then git fetch --unshallow; fi
+  # Needed to take precedence on Julia's version of Statistics
+  - julia -e 'using UUIDs; write("Project.toml", replace(read("Project.toml", String), r"uuid = .*?\n" =>"uuid = \"$(uuid4())\"\n"))'
+  - julia --project --check-bounds=yes -e 'import Pkg; Pkg.build(); Pkg.test(; coverage=true)'
 after_success:
   - julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder()); Codecov.submit(process_folder())';
 jobs:
diff --git a/Project.toml b/Project.toml
index 12c96773..0ed48328 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,8 +1,8 @@
 name = "Statistics"
-uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [extras]
diff --git a/appveyor.yml b/appveyor.yml
index e3488828..5cd86c7f 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -1,7 +1,7 @@
 environment:
   matrix:
-  - julia_version: 0.7
-  - julia_version: 1.0
+  - julia_version: 1.2
+  - julia_version: 1.3
   - julia_version: nightly
 
 platform:
@@ -29,8 +29,11 @@ install:
   - ps: iex ((new-object net.webclient).DownloadString("https://raw.githubusercontent.com/JuliaCI/Appveyor.jl/version-1/bin/install.ps1"))
 
 build_script:
-  - echo "%JL_BUILD_SCRIPT%"
-  - C:\julia\bin\julia -e "%JL_BUILD_SCRIPT%"
+  - C:\julia\bin\julia -e "using InteractiveUtils; versioninfo();
+      using UUIDs;
+      # Needed to take precedence on Julia's version of Statistics
+      write(\"Project.toml\", replace(read(\"Project.toml\", String), r"uuid = .*?\\\\n\" =>\"uuid = \\\\"$(uuid4())\\\\"\\\\n\"))'
+      import Pkg; Pkg.build()"
 
 test_script:
   - echo "%JL_TEST_SCRIPT%"
diff --git a/src/Statistics.jl b/src/Statistics.jl
index ac17bf68..7ac651dd 100644
--- a/src/Statistics.jl
+++ b/src/Statistics.jl
@@ -11,15 +11,23 @@ using LinearAlgebra, SparseArrays
 
 using Base: has_offset_axes, require_one_based_indexing
 
+using Printf: @printf
+
 export cor, cov, std, stdm, var, varm, mean!, mean,
     median!, median, middle, quantile!, quantile,
+    # moments.jl
     skewness, kurtosis,
+    # weights.jl
     AbstractWeights, Weights, AnalyticWeights, FrequencyWeights, ProbabilityWeights,
-    weights, aweights, fweights, pweights
-
+    weights, aweights, fweights, pweights,
+    # scalarstats.jl
+    geomean, harmmean, genmean, mode, modes, percentile, span, variation, sem, mad, mad!,
+    iqr, genvar, totalvar, entropy, renyientropy, crossentropy, kldivergence, describe
+    
 include("weights.jl")
 include("wsum.jl")
 include("moments.jl")
+include("scalarstats.jl")
 
 ##### mean #####
 
@@ -1193,6 +1201,17 @@ julia> quantile(skipmissing([1, 10, missing]), 0.5)
 quantile(itr, p; sorted::Bool=false, weights::Union{AbstractArray,Nothing}=nothing) =
     _quantile(itr, p, sorted, weights)
 
+"""
+    quantile(x, n::Integer)
+
+Return the n-quantiles of collection `x`, i.e. the values which
+partition `v` into `n` subsets of nearly equal size.
+
+Equivalent to `quantile(x, [0:n]/n)`. For example, `quantile(x, 5)`
+returns a vector of quantiles, respectively at `[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]`.
+"""
+quantile(x, n::Integer) = quantile(x, (0:n)/n)
+
 _quantile(itr, p, sorted::Bool, weights::Nothing) =
     quantile!(collect(itr), p, sorted=sorted)
 
@@ -1272,6 +1291,13 @@ _quantile(v::AbstractArray, p::Real, sorted::Bool, w::AbstractArray) =
 _quantile(itr, p, sorted::Bool, weights) =
     throw(ArgumentError("weights are only supported with AbstractArrays inputs"))
 
+"""
+    percentile(x, p)
+
+Return the `p`th percentile of a collection `x`, i.e. `quantile(x, p / 100)`.
+"""
+percentile(x, p) = quantile(x, p * 0.01)
+
 ##### SparseArrays optimizations #####
 
 function cov(X::SparseMatrixCSC; dims::Int=1, corrected::Bool=true)
diff --git a/src/scalarstats.jl b/src/scalarstats.jl
index cbecbb58..443a0ef7 100644
--- a/src/scalarstats.jl
+++ b/src/scalarstats.jl
@@ -165,24 +165,6 @@ end
 #
 #############################
 
-"""
-    percentile(x, p)
-
-Return the `p`th percentile of a collection `x`, i.e. `quantile(x, p / 100)`.
-"""
-percentile(x, p) = quantile(x, p * 0.01)
-
-# TODO: move to same place as other quantile methods
-"""
-    quantile(x, n::Integer)
-
-Return the n-quantiles of collection `x`, i.e. the values which
-partition `v` into `n` subsets of nearly equal size.
-
-Equivalent to `quantile(x, [0:n]/n)`. For example, `quantile(x, 5)`
-returns a vector of quantiles, respectively at `[0.0, 0.2, 0.4, 0.6, 0.8, 1.0]`.
-"""
-quantile(x, n::Integer) = quantile(x, (0:n)/n)
 
 
 #############################
@@ -230,7 +212,7 @@ end
 sem(x::AbstractArray) = sqrt(var(x, corrected=true) / length(x))
 
 # Median absolute deviation
-@irrational mad_constant 1.4826022185056018 BigFloat("1.482602218505601860547076529360423431326703202590312896536266275245674447622701")
+Base.@irrational mad_constant 1.4826022185056018 BigFloat("1.482602218505601860547076529360423431326703202590312896536266275245674447622701")
 
 """
     mad(x; center=median(x), normalize=true)
diff --git a/test/runtests.jl b/test/runtests.jl
index 11a63619..d72ace48 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -509,6 +509,15 @@ end
     @test_throws ArgumentError quantile([1, NaN], 0.5)
     @test quantile(skipmissing([1, missing, 2]), 0.5) === 1.5
 
+    @test quantile(1:5, 2) ≈ [1, 3, 5]
+    @test quantile(1:5, 4) ≈ [1:5;]
+    @test quantile(skipmissing([missing, 2, 5, missing]), 2) ≈ [2.0, 3.5, 5.0]
+    
+    @test percentile([1:5;], 25)           ≈  2.0
+    @test percentile([1:5;], [25, 50, 75]) ≈ [2.0, 3.0, 4.0]
+    @test percentile(skipmissing([missing, 2, 5, missing]), 25) ≈ 2.75
+    @test percentile(skipmissing([missing, 2, 5, missing]), [25, 50, 75]) ≈ [2.75, 3.5, 4.25]
+
     # make sure that type inference works correctly in normal cases
     for T in [Int, BigInt, Float64, Float16, BigFloat, Rational{Int}, Rational{BigInt}]
         for S in [Float64, Float16, BigFloat, Rational{Int}, Rational{BigInt}]
@@ -724,3 +733,4 @@ end
 include("weights.jl")
 include("wsum.jl")
 include("moments.jl")
+include("scalarstats.jl")
diff --git a/test/scalarstats.jl b/test/scalarstats.jl
index ca5fd6d3..cc4dfc42 100644
--- a/test/scalarstats.jl
+++ b/test/scalarstats.jl
@@ -48,19 +48,6 @@ using DelimitedFiles
 @test_throws ArgumentError mode(Any[])
 @test_throws ArgumentError modes(Any[])
 
-
-###### quantile & friends
-
-@test quantile(1:5, 2) ≈ [1, 3, 5]
-@test quantile(1:5, 4) ≈ [1:5;]
-@test quantile(skipmissing([missing, 2, 5, missing]), 2) ≈ [2.0, 3.5, 5.0]
-
-@test percentile([1:5;], 25)           ≈  2.0
-@test percentile([1:5;], [25, 50, 75]) ≈ [2.0, 3.0, 4.0]
-@test percentile(skipmissing([missing, 2, 5, missing]), 25) ≈ 2.75
-@test percentile(skipmissing([missing, 2, 5, missing]), [25, 50, 75]) ≈ [2.75, 3.5, 4.25]
-
-
 ##### Dispersion
 
 @test span([3, 4, 5, 6, 2]) == (2:6)