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Add Penalty, Regularized Least Squares and IPODRegression #37
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #37 +/- ##
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+ Coverage 84.12% 85.02% +0.90%
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Files 12 14 +2
Lines 1776 2651 +875
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+ Hits 1494 2254 +760
- Misses 282 397 +115 ☔ View full report in Codecov by Sentry. |
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Remaining comments which cannot be posted as a review comment to avoid GitHub Rate Limit
JuliaFormatter
[JuliaFormatter] reported by reviewdog 🐶
Lines 148 to 149 in 169baab
(A isa FormulaTerm) ? "formula" : | |
(A isa SparseMatrixCSC) ? "sparse " : "dense " |
[JuliaFormatter] reported by reviewdog 🐶
Lines 211 to 212 in 169baab
(A isa FormulaTerm) ? "formula" : | |
(A isa SparseMatrixCSC) ? "sparse " : "dense " |
[JuliaFormatter] reported by reviewdog 🐶
Lines 258 to 259 in 169baab
(A isa FormulaTerm) ? "formula" : | |
(A isa SparseMatrixCSC) ? "sparse " : "dense " |
[JuliaFormatter] reported by reviewdog 🐶
RobustModels.jl/test/penalties.jl
Lines 138 to 139 in 169baab
(A isa FormulaTerm) ? "formula" : | |
(A isa SparseMatrixCSC) ? "sparse " : "dense " |
[JuliaFormatter] reported by reviewdog 🐶
RobustModels.jl/test/penalties.jl
Lines 169 to 170 in 169baab
# elseif f in (confint,) | |
# @test isapprox(var1, var2; rtol=1e-1) |
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RobustModels.jl/test/underdetermined.jl
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snr::Real = 3, |
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RobustModels.jl/test/underdetermined.jl
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beta = vcat(beta, zeros(T, p-nonzero)) |
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RobustModels.jl/test/underdetermined.jl
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RobustModels.jl/test/underdetermined.jl
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X .+= sqrt(rho/(1-rho)) * z | |
X *= sqrt(1-rho) |
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RobustModels.jl/test/underdetermined.jl
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ssd = sqrt((1-rho)*sum(abs2, beta) + rho*sum(abs, beta)) |
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RobustModels.jl/test/underdetermined.jl
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RobustModels.jl/test/underdetermined.jl
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@test_throws Exception rlm(X3, y3, est1; method=method, initial_scale=1, dropcollinear=false) |
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RobustModels.jl/test/underdetermined.jl
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m1 = rlm(X3, y3, pen; method=method, initial_scale=1, maxiter=1000, dropcollinear=true) |
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RobustModels.jl/test/underdetermined.jl
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@test_throws Exception ipod(X3, y3, loss1, pen; method=method, initial_scale=1, dropcollinear=false) |
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RobustModels.jl/test/underdetermined.jl
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m2 = ipod(X3, y3, loss1, pen; method=method, initial_scale=1, maxiter=1000, dropcollinear=true) |
format use :cholesky and dropcollinear like in GLM correctly use QR test method :cholesky change types clean after rebase
Θ-IPOD Regression algorithm is based on "Outlier Detection Using Nonconvex Penalized Regression", 2011, Y. She, A.B. Owen