senresearch · GregFa · Jan 24, 2024 · Jan 24, 2024 · Jan 24, 2024
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "MatrixLMnet"
 uuid = "436227dc-6146-11e9-240b-9df6ee45b799"
 authors = ["Jane Liang", "Zifan Yu", "Gregory Farage", "Saunak Sen"]
-version = "1.0.2"
+version = "1.1.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -15,5 +15,14 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 [compat]
 DataFrames = "^1.3.0"
 MLBase = "0.8, 0.9"
-MatrixLM = "^0.1.3"
+MatrixLM = "~0.2"
+Statistics = "1"
 julia = "1.6.4, 1"
+
+[extras]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Helium = "3f79f04f-7cac-48b4-bde1-3ad54d8f74fa"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Distributions", "Helium", "Test"]
diff --git a/README.md b/README.md
@@ -82,7 +82,7 @@ alphas = reverse(collect(0:0.1:1))
 
 L<sub>1</sub> and L<sub>2</sub>-penalized estimates for matrix linear models can be obtained by running `mlmnet`. In addition to the `RawData` object, `lambdas` and `alphas`, `mlmnet` requires as a keyword argument the function name for an algorithm used to fit Elastic Net penalized estimates. Current methods are: `"cd"` (coordinate descent), `"cd_active"` (active coordinate descent), `"ista"` (ISTA with fixed step size), `"fista"` (FISTA with fixed step size), `"fista_bt"` (FISTA with backtracking), and `"admm"` (ADMM). 
 
-An object of type `Mlmnet` will be returned, with variables for the penalized coefficient estimates (`B`) along with the lambda and alpha penalty values used (`lambdas`, `alphas`). By default, `mlmnet` estimates both row and column main effects (X and Z intercepts), but this behavior can be suppressed by setting `hasXIntercept=false` and/or `hasZIntercept=false`; the intercepts will be regularized unless `toXInterceptReg=false` and/or `toZInterceptReg=false`. Individual `X` (row) and `Z` (column) effects can be left unregularized by manually passing in 1d boolean arrays of length `p` and `q` to indicate which effects should be regularized (`true`) or not (`false`) into `toXReg` and `toZReg`. By default, `mlmnet` centers and normalizes the columns of `X` and `Z` to have mean 0 and norm 1 (`toNormalize=true`). Additional keyword arguments include `isVerbose`, which controls message printing; `thresh`, the threshold at which the coefficients are considered to have converged; and `maxiter`, the maximum number of iterations. 
+An object of type `Mlmnet` will be returned, with variables for the penalized coefficient estimates (`B`) along with the lambda and alpha penalty values used (`lambdas`, `alphas`). By default, `mlmnet` estimates both row and column main effects (X and Z intercepts), but this behavior can be suppressed by setting `addXIntercept=false` and/or `addZIntercept=false`; the intercepts will be regularized unless `toXInterceptReg=false` and/or `toZInterceptReg=false`. Individual `X` (row) and `Z` (column) effects can be left unregularized by manually passing in 1d boolean arrays of length `p` and `q` to indicate which effects should be regularized (`true`) or not (`false`) into `toXReg` and `toZReg`. By default, `mlmnet` centers and normalizes the columns of `X` and `Z` to have mean 0 and norm 1 (`toNormalize=true`). Additional keyword arguments include `isVerbose`, which controls message printing; `thresh`, the threshold at which the coefficients are considered to have converged; and `maxiter`, the maximum number of iterations. 
 
 ```
 est = mlmnet(dat, lambdas, alphas, method = "fista_bt")

diff --git a/src/bic/mlmnet_bic.jl b/src/bic/mlmnet_bic.jl
@@ -31,7 +31,7 @@ end
                    lambdas::AbstractArray{Float64,1},
                    alphas::AbstractArray{Float64,1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -55,9 +55,9 @@ Performs BIC validation for `mlmnet`.
   default is `ista`, and the other methods are `fista`, `fista_bt`, `admm` and `cd`
 - isNaive = boolean flag indicating whether to solve the Naive or non-Naive 
   Elastic-net problem
-- hasXIntercept = boolean flag indicating whether or not to include an `X` 
+- addXIntercept = boolean flag indicating whether or not to include an `X` 
   intercept (row main effects). Defaults to `true`. 
-- hasZIntercept = boolean flag indicating whether or not to include a `Z` 
+- addZIntercept = boolean flag indicating whether or not to include a `Z` 
   intercept (column main effects). Defaults to `true`.
 - toXReg = 1d array of bit flags indicating whether or not to regularize each 
   of the `X` (row) effects. Defaults to 2d array of `true`s with length 
@@ -94,7 +94,7 @@ function mlmnet_bic(data::RawData,
                    lambdas::AbstractArray{Float64,1},
                    alphas::AbstractArray{Float64,1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -107,8 +107,8 @@ function mlmnet_bic(data::RawData,
     # Run mlmnet on each RawData object, in parallel when possible
     MLMNet = mlmnet(data, lambdas, alphas;
                       method=method, isNaive=isNaive, 
-                      hasXIntercept=hasXIntercept, 
-                      hasZIntercept=hasZIntercept, 
+                      addXIntercept=addXIntercept, 
+                      addZIntercept=addZIntercept, 
                       toXReg=toXReg, toZReg=toZReg, 
                       toXInterceptReg=toXInterceptReg, 
                       toZInterceptReg=toZInterceptReg, 

diff --git a/src/crossvalidation/mlmnet_cv.jl b/src/crossvalidation/mlmnet_cv.jl
@@ -41,7 +41,7 @@ end
                    rowFolds::Array{Array{Int64,1},1}, 
                    colFolds::Array{Array{Int64,1},1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -74,9 +74,9 @@ input.
   default is `ista`, and the other methods are `fista`, `fista_bt`, `admm` and `cd`
 - isNaive = boolean flag indicating whether to solve the Naive or non-Naive 
   Elastic-net problem
-- hasXIntercept = boolean flag indicating whether or not to include an `X` 
+- addXIntercept = boolean flag indicating whether or not to include an `X` 
   intercept (row main effects). Defaults to `true`. 
-- hasZIntercept = boolean flag indicating whether or not to include a `Z` 
+- addZIntercept = boolean flag indicating whether or not to include a `Z` 
   intercept (column main effects). Defaults to `true`.
 - toXReg = 1d array of bit flags indicating whether or not to regularize each 
   of the `X` (row) effects. Defaults to 2d array of `true`s with length 
@@ -116,7 +116,7 @@ function mlmnet_cv(data::RawData,
                    rowFolds::Array{Array{Int64,1},1}, 
                    colFolds::Array{Array{Int64,1},1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -156,8 +156,8 @@ function mlmnet_cv(data::RawData,
 
     MLMNets = Distributed.pmap(data -> mlmnet(data, lambdas, alphas;
                                               method=method, isNaive=isNaive, 
-                                              hasXIntercept=hasXIntercept, 
-                                              hasZIntercept=hasZIntercept, 
+                                              addXIntercept=addXIntercept, 
+                                              addZIntercept=addZIntercept, 
                                               toXReg=toXReg, toZReg=toZReg, 
                                               toXInterceptReg=toXInterceptReg, 
                                               toZInterceptReg=toZInterceptReg, 
@@ -177,7 +177,7 @@ end
                    alphas::AbstractArray{Float64,1},
                    rowFolds::Array{Array{Int64,1},1}, nColFolds::Int64;
                    method::String="ista", isNaive::Bool=false, 
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -207,9 +207,9 @@ Calls the base `mlmnet_cv` function.
 
 - methods = function name that applies the Elastic-net penalty estimate method;
   default is `ista`, and the other methods are `fista`, `fista_bt`, `admm` and `cd`
-- hasXIntercept = boolean flag indicating whether or not to include an `X` 
+- addXIntercept = boolean flag indicating whether or not to include an `X` 
   intercept (row main effects). Defaults to `true`. 
-- hasZIntercept = boolean flag indicating whether or not to include a `Z` 
+- addZIntercept = boolean flag indicating whether or not to include a `Z` 
   intercept (column main effects). Defaults to `true`.
 - toXReg = 1d array of bit flags indicating whether or not to regularize each 
   of the `X` (row) effects. Defaults to 2d array of `true`s with length 
@@ -248,7 +248,7 @@ function mlmnet_cv(data::RawData,
                    alphas::AbstractArray{Float64,1},
                    rowFolds::Array{Array{Int64,1},1}, nColFolds::Int64;
                    method::String="ista", isNaive::Bool=false, 
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -263,7 +263,7 @@ function mlmnet_cv(data::RawData,
     # base mlmnet_cv function
     mlmnet_cv(data, lambdas, alphas, rowFolds, colFolds; 
               method=method, isNaive=isNaive,
-              hasXIntercept=hasXIntercept, hasZIntercept=hasZIntercept, 
+              addXIntercept=addXIntercept, addZIntercept=addZIntercept, 
               toXReg=toXReg, toZReg=toZReg, 
               toXInterceptReg=toXInterceptReg, 
               toZInterceptReg=toZInterceptReg, 
@@ -278,7 +278,7 @@ end
                    alphas::AbstractArray{Float64,1}, 
                    nRowFolds::Int64, colFolds::Array{Array{Int64,1},1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -308,9 +308,9 @@ input. Calls the base `mlmnet_cv` function.
 
 - methods = function name that applies the Elastic-net penalty estimate method;
   default is `ista`, and the other methods are `fista`, `fista_bt`, `admm` and `cd`
-- hasXIntercept = boolean flag indicating whether or not to include an `X` 
+- addXIntercept = boolean flag indicating whether or not to include an `X` 
   intercept (row main effects). Defaults to `true`. 
-- hasZIntercept = boolean flag indicating whether or not to include a `Z` 
+- addZIntercept = boolean flag indicating whether or not to include a `Z` 
   intercept (column main effects). Defaults to `true`.
 - toXReg = 1d array of bit flags indicating whether or not to regularize each 
   of the `X` (row) effects. Defaults to 2d array of `true`s with length 
@@ -349,7 +349,7 @@ function mlmnet_cv(data::RawData,
                    alphas::AbstractArray{Float64,1}, 
                    nRowFolds::Int64, colFolds::Array{Array{Int64,1},1}; 
                    method::String="ista", isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -364,7 +364,7 @@ function mlmnet_cv(data::RawData,
     # base mlmnet_cv function
     mlmnet_cv(data, lambdas, alphas, rowFolds, colFolds; 
               method=method, isNaive=isNaive,
-              hasXIntercept=hasXIntercept, hasZIntercept=hasZIntercept, 
+              addXIntercept=addXIntercept, addZIntercept=addZIntercept, 
               toXReg=toXReg, toZReg=toZReg, 
               toXInterceptReg=toXInterceptReg, 
               toZInterceptReg=toZInterceptReg, 
@@ -379,7 +379,7 @@ end
                    nRowFolds::Int64=10, nColFolds::Int64=10;
                    method::String="ista", 
                    isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -409,9 +409,9 @@ folds randomly generated using calls to `make_folds`. Calls the base
 
 - methods = function name that applies the Elastic-net penalty estimate method;
   default is `ista`, and the other methods are `fista`, `fista_bt`, `admm` and `cd`
-- hasXIntercept = boolean flag indicating whether or not to include an `X` 
+- addXIntercept = boolean flag indicating whether or not to include an `X` 
   intercept (row main effects). Defaults to `true`. 
-- hasZIntercept = boolean flag indicating whether or not to include a `Z` 
+- addZIntercept = boolean flag indicating whether or not to include a `Z` 
   intercept (column main effects). Defaults to `true`.
 - toXReg = 1d array of bit flags indicating whether or not to regularize each 
   of the `X` (row) effects. Defaults to 2d array of `true`s with length 
@@ -465,7 +465,7 @@ function mlmnet_cv(data::RawData,
                    nRowFolds::Int64=10, nColFolds::Int64=10;
                    method::String="ista", 
                    isNaive::Bool=false,
-                   hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                   addXIntercept::Bool=true, addZIntercept::Bool=true, 
                    toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                    toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                    toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -481,7 +481,7 @@ function mlmnet_cv(data::RawData,
     # function
     mlmnet_cv(data, lambdas, alphas, rowFolds, colFolds; 
               method=method, isNaive=isNaive, 
-              hasXIntercept=hasXIntercept, hasZIntercept=hasZIntercept, 
+              addXIntercept=addXIntercept, addZIntercept=addZIntercept, 
               toXReg=toXReg, toZReg=toZReg, 
               toXInterceptReg=toXInterceptReg, 
               toZInterceptReg=toZInterceptReg, 
@@ -496,7 +496,7 @@ end
                       nRowFolds::Int64=10, nColFolds::Int64=10;
                       method::String="ista", 
                       isNaive::Bool=false,
-                      hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                      addXIntercept::Bool=true, addZIntercept::Bool=true, 
                       toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                       toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                       toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -516,7 +516,7 @@ function mlmnet_cv(data::RawData,
                       nRowFolds::Int64=10, nColFolds::Int64=10;
                       method::String="ista", 
                       isNaive::Bool=false,
-                      hasXIntercept::Bool=true, hasZIntercept::Bool=true, 
+                      addXIntercept::Bool=true, addZIntercept::Bool=true, 
                       toXReg::BitArray{1}=trues(size(get_X(data), 2)), 
                       toZReg::BitArray{1}=trues(size(get_Z(data), 2)), 
                       toXInterceptReg::Bool=false, toZInterceptReg::Bool=false, 
@@ -532,7 +532,7 @@ function mlmnet_cv(data::RawData,
     # function
     mlmnet_cv(data, lambdas, alphas, nRowFolds, nColFolds; 
                   method=method, isNaive=isNaive, 
-                  hasXIntercept=hasXIntercept, hasZIntercept=hasZIntercept, 
+                  addXIntercept=addXIntercept, addZIntercept=addZIntercept, 
                   toXReg=toXReg, toZReg=toZReg, 
                   toXInterceptReg=toXInterceptReg, 
                   toZInterceptReg=toZInterceptReg,