Remove depreciations (#115)

* update to FixedEffects 0.7.4 * up * Update .gitlab-ci.yml * update * remove deprecated * Update README.md
FixedEffects · Aug 5, 2020 · e3aa1e2 · e3aa1e2 · matthieugomez · Aug 6, 2020
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diff --git a/.DS_Store b/.DS_Store
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
@@ -3,5 +3,5 @@ variables:
 
 include:
   - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v4/common.yml'
-  - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v4/test_v1.1.yml'
+  - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v4/test_v1.3.yml'
   - 'https://raw.githubusercontent.com/JuliaGPU/gitlab-ci/master/templates/v4/test_dev.yml'
diff --git a/Project.toml b/Project.toml
@@ -1,6 +1,6 @@
 name = "FixedEffectModels"
 uuid = "9d5cd8c9-2029-5cab-9928-427838db53e3"
-version = "0.10.9"
+version = "0.11.0"
 
 [deps]
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
@@ -18,15 +18,15 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-Combinatorics = "0, 1.0"
-DataFrames = "0.19, 0.20, 0.21"
+Combinatorics = "0, 1"
+DataFrames = "0.21"
 Distributions = "0"
 FillArrays = "0"
 FixedEffects = "0.7.4"
 Reexport = "0"
 StatsBase = "0.32, 0.33"
 StatsModels = "0.6"
-Tables = "0, 1.0"
+Tables = "1"
 julia = "1.3"
 
 [extras]

diff --git a/README.md b/README.md
@@ -1,7 +1,6 @@
 [![Build Status](https://travis-ci.com/FixedEffects/FixedEffectModels.jl.svg?branch=master)](https://travis-ci.com/FixedEffects/FixedEffectModels.jl)
-[![pipeline status](https://gitlab.com/JuliaGPU/FixedEffectModels.jl/badges/master/pipeline.svg)](https://gitlab.com/JuliaGPU/FixedEffectModels.jl/commits/master)
 
-This package estimates linear models with high dimensional categorical variables and/or instrumental variables. 
+This package estimates linear models with high dimensional categorical variables and/or instrumental variables.
 
 Its objective is similar to the Stata command [`reghdfe`](https://github.com/sergiocorreia/reghdfe) and the R function [`felm`](https://cran.r-project.org/web/packages/lfe/lfe.pdf). The package tends to be much faster than these two options.
 
@@ -31,13 +30,13 @@ reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State), weig
 
 
 -  A typical formula is composed of one dependent variable, exogeneous variables, endogeneous variables, instrumental variables, and a set of high-dimensional fixed effects.
-		
+
 	```julia
 	dependent variable ~ exogenous variables + (endogenous variables ~ instrumental variables) + fe(fixedeffect variable)
 	```
 
-	High-dimensional fixed effect variables are indicated with the function `fe`.  You can add an arbitrary number of high dimensional fixed effects, separated with `+`. You can also interact fixed effects using `&` or `*`. 
-	
+	High-dimensional fixed effect variables are indicated with the function `fe`.  You can add an arbitrary number of high dimensional fixed effects, separated with `+`. You can also interact fixed effects using `&` or `*`.
+
 	For instance, to add state fixed effects use `fe(State)`. To add both state and year fixed effects, use `fe(State) + fe(Year)`. To add state-year fixed effects, use `fe(State)&fe(Year)`. To add state specific slopes for year, use `fe(State)&Year`. To add both state fixed-effects and state specific slopes for year use `fe(State)*Year`.
 
 	```julia
@@ -62,22 +61,22 @@ reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State), weig
 	```julia
 	weights = :Pop
 	```
-- The option `subset` specifies a subset of the data 
+- The option `subset` specifies a subset of the data
 	```julia
 	subset = df.State .>= 30
 	```
 - The option `save` can be set to one of the following:  `:residuals` to save residuals, `:fe` to save fixed effects, `true` to save both. You can access the result with `residuals()` and `fe()`
 
 - The option `method` can be set to one of the following: `:cpu`, `:gpu` (see Performances below).
 
-- The option `contrasts` specifies particular contrasts for categorical variables in the formula, e.g. 
+- The option `contrasts` specifies particular contrasts for categorical variables in the formula, e.g.
 	```julia
 	df.YearC = categorical(df.Year)
 	reg(df, @formula(Sales ~ YearC); contrasts = Dict(:YearC => DummyCoding(base = 80)))
 	```
 ## Output
-`reg` returns a light object. It is composed of 
- 
+`reg` returns a light object. It is composed of
+
   - the vector of coefficients & the covariance matrix (use `coef`, `coefnames`, `vcov` on the output of `reg`)
   - a boolean vector reporting rows used in the estimation
   - a set of scalars (number of observations, the degree of freedoms, r2, etc)
@@ -90,13 +89,10 @@ Methods such as `predict`, `residuals` are still defined but require to specify
 You may use [RegressionTables.jl](https://github.com/jmboehm/RegressionTables.jl) to get publication-quality regression tables.
 
 
-## Performances
-#### GPU
+## GPU
 The package has support for GPUs (Nvidia) (thanks to Paul Schrimpf). This can make the package an order of magnitude faster for complicated problems.
 
-First make sure to do `using CUDA` before `using FixedEffectModels`. Then, estimate a model with `method = :gpu`.
-
-When working on the GPU, it is encouraged to set the floating point precision to `Float32` with `double_precision = false`, since it is usually much faster.
+To use GPU, run `using CUDA` before `using FixedEffectModels`. Then, estimate a model with `method = :gpu`. For maximum speed, set the floating point precision to `Float32` with `double_precision = false`.
 
 ```julia
 using CUDA, FixedEffectModels
@@ -124,10 +120,6 @@ Fong, DC. and Saunders, M. (2011) *LSMR: An Iterative Algorithm for Sparse Least
 
 Gaure, S. (2013) *OLS with Multiple High Dimensional Category Variables*. Computational Statistics and Data Analysis
 
-Kleibergen, F, and Paap, R. (2006) *Generalized reduced rank tests using the singular value decomposition.* Journal of econometrics 
+Kleibergen, F, and Paap, R. (2006) *Generalized reduced rank tests using the singular value decomposition.* Journal of econometrics
 
 Kleibergen, F. and Schaffer, M.  (2007) *RANKTEST: Stata module to test the rank of a matrix using the Kleibergen-Paap rk statistic*. Statistical Software Components, Boston College Department of Economics.
-
-
-
-
diff --git a/src/FixedEffectModels.jl b/src/FixedEffectModels.jl
@@ -6,7 +6,6 @@ module FixedEffectModels
 ## Dependencies
 ##
 ##############################################################################
-using Base
 using LinearAlgebra
 using Statistics
 using Printf
@@ -15,11 +14,10 @@ using DataFrames
 using Distributions
 using Reexport
 using Tables
+using FixedEffects
 @reexport using StatsBase
 @reexport using StatsModels
 
-using FixedEffects
-
 ##############################################################################
 ##
 ## Exported methods and types
@@ -29,16 +27,10 @@ using FixedEffects
 export reg,
 partial_out,
 fe,
-
 FixedEffectModel,
 has_iv,
 has_fe,
-
-Vcov,
-
-#deprecated
-@model,
-fes
+Vcov
 
 ##############################################################################
 ##
@@ -58,6 +50,5 @@ include("FixedEffectModel.jl")
 include("fit.jl")
 include("partial_out.jl")
 
-include("deprecated.jl")
 
 end  # module FixedEffectModels
diff --git a/src/deprecated.jl b/src/deprecated.jl
diff --git a/src/fit.jl b/src/fit.jl
@@ -43,24 +43,9 @@ function reg(@nospecialize(df),
     method::Symbol = :cpu, 
     drop_singletons = true, 
     double_precision::Bool = true, 
-    tol::Real = double_precision ? 1e-8 : 1e-6,
-    @nospecialize(feformula::Union{Symbol, Expr, Nothing} = nothing),
-    @nospecialize(vcovformula::Union{Symbol, Expr, Nothing} = nothing),
-    @nospecialize(subsetformula::Union{Symbol, Expr, Nothing} = nothing))
+    tol::Real = double_precision ? 1e-8 : 1e-6)
+
     df = DataFrame(df; copycols = false) 
-    # to deprecate
-    if vcovformula != nothing
-        if (vcovformula == :simple) | (vcovformula == :(simple()))
-            vcov = Vcov.Simple()
-        elseif (vcovformula == :robust) | (vcovformula == :(robust()))
-            vcov = Vcov.Robust()
-        else
-            vcov = Vcov.cluster(StatsModels.termvars(@eval(@formula(0 ~ $(vcovformula.args[2]))))...)
-        end
-    end
-    if subsetformula != nothing
-        subset = eval(evaluate_subset(df, subsetformula))
-    end
 
     ##############################################################################
     ##
@@ -96,9 +81,6 @@ function reg(@nospecialize(df),
 
     # create a dataframe without missing values & negative weights
     vars = StatsModels.termvars(formula)
-    if feformula != nothing # to deprecate
-        vars = vcat(vars, StatsModels.termvars(@eval(@formula(0 ~ $(feformula)))))
-    end
     iv_vars = Symbol[]
     endo_vars = Symbol[]
     if has_iv
@@ -121,11 +103,6 @@ function reg(@nospecialize(df),
     end
     fes, ids, formula = parse_fixedeffect(df, formula)
     has_fes = !isempty(fes)
-    if feformula != nothing
-        has_fes = true
-        feformula = @eval(@formula(0 ~ $(feformula)))
-        fes, ids = oldparse_fixedeffect(df, feformula)
-    end
     if has_fes
         if drop_singletons
             for fe in fes