%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  haldensify
%global packver   0.2.8
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.2.8
Release:          1%{?dist}%{?buildtag}
Summary:          Highly Adaptive Lasso Conditional Density Estimation

License:          MIT + file LICENSE
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 3.2.0
Requires:         R-core >= 3.2.0
BuildArch:        noarch
BuildRequires:    R-CRAN-origami >= 1.0.7
BuildRequires:    R-CRAN-hal9001 >= 0.4.6
BuildRequires:    R-stats 
BuildRequires:    R-utils 
BuildRequires:    R-CRAN-dplyr 
BuildRequires:    R-CRAN-tibble 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-data.table 
BuildRequires:    R-CRAN-matrixStats 
BuildRequires:    R-CRAN-future.apply 
BuildRequires:    R-CRAN-assertthat 
BuildRequires:    R-CRAN-stringr 
BuildRequires:    R-CRAN-rlang 
BuildRequires:    R-CRAN-scales 
BuildRequires:    R-CRAN-Rdpack 
Requires:         R-CRAN-origami >= 1.0.7
Requires:         R-CRAN-hal9001 >= 0.4.6
Requires:         R-stats 
Requires:         R-utils 
Requires:         R-CRAN-dplyr 
Requires:         R-CRAN-tibble 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-data.table 
Requires:         R-CRAN-matrixStats 
Requires:         R-CRAN-future.apply 
Requires:         R-CRAN-assertthat 
Requires:         R-CRAN-stringr 
Requires:         R-CRAN-rlang 
Requires:         R-CRAN-scales 
Requires:         R-CRAN-Rdpack 

%description
An algorithm for flexible conditional density estimation based on
application of pooled hazard regression to an artificial repeated measures
dataset constructed by discretizing the support of the outcome variable.
To facilitate flexible estimation of the conditional density, the highly
adaptive lasso, a non-parametric regression function shown to estimate
cadlag (RCLL) functions at a suitably fast convergence rate, is used. The
use of pooled hazards regression for conditional density estimation as
implemented here was first described for by Díaz and van der Laan (2011)
<doi:10.2202/1557-4679.1356>. Building on the conditional density
estimation utilities, non-parametric inverse probability weighted (IPW)
estimators of the causal effects of additive modified treatment policies
are implemented, using conditional density estimation to estimate the
generalized propensity score. Non-parametric IPW estimators based on this
can be coupled with undersmoothing of the generalized propensity score
estimator to attain the semi-parametric efficiency bound (per Hejazi,
Díaz, and van der Laan <doi:10.48550/arXiv.2205.05777>).

%prep
%setup -q -c -n %{packname}

# fix end of executable files
find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;
# prevent binary stripping
[ -d %{packname}/src ] && find %{packname}/src -type f -exec \
  sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true
[ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \
  sed -i 's@-g0@@g' {} \; || true
# don't allow local prefix in executable scripts
find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \;

%build

%install

mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
# remove buildroot from installed files
find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \;

%files
%{rlibdir}/%{packname}