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## Using modified forks to test code (mostly fixed depreciations, but can inspect the changes at the link)

using DataFrames # From https://github.com/JuliaEconometrics/DataFrames.jl
using Feather
using StatsModels # From https://github.com/JuliaEconometrics/StatsModels.jl

df = Feather.read("NLS.feather") # Feather file from Stata's webuse nlswork
df = df[[:idcode, :year, :ln_wage, :age, :race, :union, :ttl_exp]]
df[:idcode] = string.(df[:idcode])
df[:year] = string.(df[:year])

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## This skeleton provides a framework to develop a benchmark for various routines using QRupdate

## The following example tests the efficiency of obtaining the subset of linearly independent columns from
## various matrices. An application of these procedures is for instrumental least squares estimators (e.g., 2SLS).

using QRupdate
srand(0)
m = Int(1e6)
X = rand(m, 3)

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if (!any(grepl(pattern = 'pacman', x = installed.packages()[,1]))) {
  install.packages('pacman')
}
pacman::p_load(dplyr, plm, multiwayvcov)
df = data(Grunfeld, package = 'plm')
model = lm(formula = value ~ inv + capital,
           data = Grunfeld)
vcov_both = multiwayvcov::cluster.vcov(model, select(Grunfeld, firm, year))
diag(vcov_both)[2:3]

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use "http://www.stata-press.com/data/r13/grunfeld", clear
reghdfe mvalue invest kstock, noab cl(company year)
vce

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# This is the draft code for generalizing GLM to vector generalized linear models (VGLM)
# Compared to Stata's mlogit (13.1) and R's multinom {nnet}, need to verify and correct mlogit vcov
# Code tested with Julia v"0.7.0-DEV.4993"

# Special Matrices
using LinearAlgebra: Hermitian, LowerTriangular, UpperTriangular, Diagonal
# Factorizations
using LinearAlgebra: Factorization, qrfact, cholfact!, QRPivoted, norm
# Functions
using LinearAlgebra: mul!, diag, copytri!

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# Author: José Bayoán Santiago Calderón (@Nosferican)

# Set up the environment for this application

using Distributions: Normal, TDist
using Plots: Plot, PyPlotBackend, plot, density, savefig, plot!, pyplot
using StatPlots: StatPlots, @df
using Random: seed!
using Statistics: mean, quantile
using StatsBase: coef, coefnames, confint, countmap, dof_residual, levelsmap,

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using Pkg.TOML: parsefile
using Base.Iterators: flatten

pkgs = ((d for d in readdir(dir) if occursin(r"^[(aA-zZ|0-9)]+$", d))
           for dir in joinpath.(first(DEPOT_PATH),"registries","General", string.('A':'Z'))) |>
       flatten |>
       collect

"""
    cap(v::AbstractString)

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/*** SAS code for examples in Chapter 4 of                             ***/
/*** Bioequivalence and Statistics in Clinical Pharmacology,           ***/
/*** by Scott Patterson and Byron Jones,***/
/*** Chapman and Hall/CRC Press: Boca Raton, London and New York, 2005.***/


/*** WARNING : PROGRAM OFFERED FOR USE WITHOUT ANY GUARANTEES    ***/
/*** NO LIABILITY IS ACCEPTED FOR ANY LOSS RESULTING FROM USE OF ***/
/*** THIS SET OF SAS INTRUCTIONS                                ***/

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