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pssst

Functions to fit parametric survival models to survey data to estimate finite-population, synthetic child mortality across multiple time periods.

Overview

pssst is an open-source R package containing functions to fit parametric survival models to survey data to estimate finite-population, synthetic child mortality across multiple time periods.

The package currently supports the following types of survival data:

  • Interval-censored
  • Right-censored
  • Exactly observed

The package currently supports the following parametric distributions:

  • Exponential
  • Weibull
  • Piecewise Exponential
  • Generalized Gamma
  • Lognormal
  • Gompertz
  • Dagum
  • Log-logistic
  • Exponentially-truncated shifted power family

Installation

The current development version can be installed using devtools::install_github():

devtools::install_github(repo="taylorokonek/pssst")

Common Installation Error

If you receive an error that contains the following message...

ld: warning: directory not found for option '-L/opt/gfortran/lib/gcc/aarch64-apple-darwin20.0/12.2.0'
ld: warning: directory not found for option '-L/opt/gfortran/lib'
ld: library not found for -lgfortran

... you likely have a Mac with an M1 chip, and it is your first time installing a package that depends on c++ code! The fix for this is typically the following (steps taking from this post):

  1. Create a ~/.R/Makevars file on your machine, if one does not already exist

  2. Open the Makevars file in your favorite text editor, and add the following lines to it:

LOC = /usr/local/gfortran
CC=$(LOC)/bin/gcc -fopenmp
CXX=$(LOC)/bin/g++ -fopenmp
CXX11 = $(LOC)/bin/g++ -fopenmp # for fst package
CFLAGS=-g -O3 -Wall -pedantic -std=gnu99 -mtune=native -pipe
CXXFLAGS=-g -O3 -Wall -pedantic -std=c++11 -mtune=native -pipe
LDFLAGS=-L$(LOC)/lib -Wl,-rpath,$(LOC)/lib
CPPFLAGS=-I$(LOC)/include -I/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include
  1. Save the file, and try re-installing pssst. If it still doesn't work, please create an Issue on this github page so we can address this!

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Parametric Survey-weighted Survival models for Synthetic children across Time

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