diff --git a/docs/src/examples.md b/docs/src/examples.md
index d70062e..be54c9f 100644
--- a/docs/src/examples.md
+++ b/docs/src/examples.md
@@ -7,6 +7,9 @@ rds = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "1fptime.
 
 rds2 = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "ftdf3.csv"); types = [String,  Float64, Float64, String, String, String, String, String, Float64]) |> DataFrame
 
+
+devday = CSV.File(joinpath(dirname(pathof(Metida)), "..", "test", "csv",  "devday.csv"); types = [Float64, String, String, String ]) |> DataFrame
+
 nothing; # hide
 ```
 
@@ -197,3 +200,50 @@ MixedModels result:
 mm = fit(MixedModel, @formula(response ~ factor+ (0+r1|subject)), rds2, REML = true)
 println(mm) #hide
 ```
+
+## Aumented covariance (Experimental)
+
+Covariance modificator `ACOV()` can be used as second repeated effect. In this case covariance calculated with existed matrix, 
+that was build at previous step. For example addition `ACOV(AR)` to `DIAG` structure is the same as `ARH` if same blocking factor used.
+
+```@example lmmexample
+   lmm1 = Metida.LMM(@formula(response ~ 1), rds2;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.DIAG), Metida.VarEffect(Metida.@covstr(1|subject), Metida.ACOV(Metida.AR))]
+    )
+    Metida.fit!(lmm1)
+```
+
+```@example lmmexample
+    lmm2 = Metida.LMM(@formula(response ~ 1), rds2;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.ARH)]
+    )
+    Metida.fit!(lmm2)
+```
+
+R-part of variance-covariance matrix:
+
+```@example lmmexample
+Metida.rmatrix(lmm1, 1)
+```
+
+If nested blocking factor used - covariance modification applyed only within that blocks:
+
+```@example lmmexample
+   lmm = Metida.LMM(@formula(response ~ 1), rds2;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.DIAG), Metida.VarEffect(Metida.@covstr(1|subject), Metida.ACOV(Metida.AR))]
+    )
+Metida.fit!(lmm)
+Metida.rmatrix(lmm, 1)
+```
+
+For nested models covariance structure can be expanded as follows:
+* the first layer describes unstructured the device-device covariance;
+* the second layer adds the time covariance for each device
+
+```@example lmmexample
+lmm = Metida.LMM(@formula(resp ~ 0 + device), devday;
+    repeated = [Metida.VarEffect(Metida.@covstr(device|subj&day), Metida.UN), 
+    Metida.VarEffect(Metida.@covstr(1|subj&device), Metida.ACOV(Metida.AR))]
+    )
+    Metida.fit!(lmm)
+```
\ No newline at end of file
diff --git a/src/rmat.jl b/src/rmat.jl
index f0e22b9..f86d3d1 100644
--- a/src/rmat.jl
+++ b/src/rmat.jl
@@ -349,9 +349,9 @@ function rmat!(mx, θ, ::AbstractMatrix, ::ACOV_{AR_}, ::Int)
     ρ  = θ[1]
     if s > 1
         for n = 2:s
-            mxnn = mx[n, n]
+            mxnn = sqrt(mx[n, n])
             @inbounds @simd for m = 1:n-1
-                mxmm = mx[m, m]
+                mxmm = sqrt(mx[m, m])
                 mx[m, n] += mxnn * mxmm * ρ ^ (n - m)
             end
         end
@@ -365,9 +365,9 @@ function rmat!(mx, θ, ::AbstractMatrix, ::ACOV_{CS_}, ::Int)
     ρ  = θ[1]
     if s > 1
         for n = 2:s
-            mxnn = mx[n, n]
+            mxnn = sqrt(mx[n, n])
             @inbounds @simd for m = 1:n-1
-                mxmm = mx[m, m]
+                mxmm = sqrt(mx[m, m])
                 mx[m, n] += mxnn * mxmm * ρ 
             end
         end
diff --git a/test/csv/devday.csv b/test/csv/devday.csv
new file mode 100644
index 0000000..9b17f18
--- /dev/null
+++ b/test/csv/devday.csv
@@ -0,0 +1,201 @@
+resp,device,day,subj
+8.72644141172539,A,d1,1
+5.042766927404885,A,d2,1
+4.186325590586714,B,d1,1
+1.5284483141734233,B,d2,1
+0.9795814766598748,C,d1,1
+-0.9859211795111515,C,d2,1
+1.815722019857775,D,d1,1
+0.6483360099159381,D,d2,1
+7.678877996324462,A,d1,2
+2.6894607393966203,A,d2,2
+1.604802444054944,B,d1,2
+0.06477952808780052,B,d2,2
+0.08761542039857528,C,d1,2
+-1.3153348196357393,C,d2,2
+-0.9458496052444734,D,d1,2
+-2.3047486276895612,D,d2,2
+5.450560693901975,A,d1,3
+9.686372619790028,A,d2,3
+1.718464421060092,B,d1,3
+0.030382263239601137,B,d2,3
+-2.4141035515735343,C,d1,3
+0.6145971257996419,C,d2,3
+-3.515779754752826,D,d1,3
+-0.7924377776322691,D,d2,3
+3.813544710443301,A,d1,4
+12.777701384288903,A,d2,4
+3.154188215710133,B,d1,4
+2.4545212975960635,B,d2,4
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+-0.7744416190640289,D,d1,4
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+-1.2647252830578548,C,d1,5
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+1.3253048725047756,D,d2,5
+5.145425009462966,A,d1,6
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+-2.223309642785237,B,d1,6
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+8.397112273554288,A,d1,7
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+-3.0685326916867455,D,d1,9
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diff --git a/test/test.jl b/test/test.jl
index 8b8139b..ff51069 100644
--- a/test/test.jl
+++ b/test/test.jl
@@ -782,6 +782,54 @@ end
     @test_nowarn Metida.fit!(lmm; varlinkf = :identity)
 end
 
+@testset "  Model: Augmented covariance                              " begin
+    lmm1 = Metida.LMM(@formula(response ~ 1), ftdf3;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.DIAG), Metida.VarEffect(Metida.@covstr(1|subject), Metida.ACOV(Metida.AR))]
+    )
+    Metida.fit!(lmm1)
+
+    lmm2 = Metida.LMM(@formula(response ~ 1), ftdf3;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.ARH)]
+    )
+    Metida.fit!(lmm2)
+    @test Metida.m2logreml(lmm1)  ≈ Metida.m2logreml(lmm2) 
+
+
+    lmm1 = Metida.LMM(@formula(response ~ 1), ftdf3;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.DIAG), Metida.VarEffect(Metida.@covstr(1|subject), Metida.ACOV(Metida.CS))]
+    )
+    Metida.fit!(lmm1)
+
+    lmm2 = Metida.LMM(@formula(response ~ 1), ftdf3;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.CSH)]
+    )
+    Metida.fit!(lmm2)
+    @test Metida.m2logreml(lmm1)  ≈ Metida.m2logreml(lmm2) 
+
+    lmm = Metida.LMM(@formula(response ~ 1), ftdf3;
+    repeated = [Metida.VarEffect(Metida.@covstr(r1|subject), Metida.DIAG), Metida.VarEffect(Metida.@covstr(1|subject&r2), Metida.ACOV(Metida.AR))]
+    )
+    Metida.fit!(lmm)
+    @test Metida.m2logreml(lmm)  ≈ 800.7606945922405 atol=1E-6
+
+
+    lmm1 = Metida.LMM(@formula(resp ~ 0 + device), devday;
+    repeated = [Metida.VarEffect(Metida.@covstr(device|subj&day), Metida.UN), 
+    Metida.VarEffect(Metida.@covstr(1|subj&device), Metida.ACOV(Metida.CS))]
+    )
+    Metida.fit!(lmm1)
+
+    lmm2 = Metida.LMM(@formula(resp ~ 0 + device), devday;
+    repeated = [Metida.VarEffect(Metida.@covstr(device|subj&day), Metida.UN), 
+    Metida.VarEffect(Metida.@covstr(1|subj&device), Metida.ACOV(Metida.AR))]
+    )
+    Metida.fit!(lmm2)
+    @test Metida.m2logreml(lmm1)  ≈ Metida.m2logreml(lmm2) 
+
+end
+
+
+
 @testset "  Random                                                   " begin
     lmm = Metida.LMM(@formula(response ~ 1 + factor*time), ftdf2;
     random = Metida.VarEffect(Metida.@covstr(factor|subject&factor), Metida.DIAG),
diff --git a/test/testdata.jl b/test/testdata.jl
index 4d6077d..ba1d9d3 100644
--- a/test/testdata.jl
+++ b/test/testdata.jl
@@ -17,6 +17,7 @@ ftdf3        = CSV.File(path*"/csv/ftdf3.csv"; types =
 
 spatdf       = CSV.File(path*"/csv/spatialdata.csv"; types = [Int, Int, String, Float64, Float64, Float64, Float64, Float64]) |> DataFrame
 
+devday       = CSV.File(path*"/csv/devday.csv"; types = [ Float64, String, String, String]) |> DataFrame
 
 dfrdsfda        = CSV.File(joinpath(path, "csv", "berds", "rds1.csv"), types = Dict(:PK => Float64, :subject => String, :period => String, :sequence => String, :treatment => String )) |> DataFrame
 dropmissing!(dfrdsfda)