README.md
In xuyiqing/tjbal: Trajectory Balancing

tjbal

R package for implementing trajectory balancing, a kernel-based reweighting method for causal inference with panel data.

Repo: GitHub

Examples: R code used in the tutorial can be downloaded from here.

Reference: Hazlett, Chad and Yiqing Xu, 2018. “Trajectory Balancing: A General Reweighting Approach to Causal Inference with Time-Series Cross-Sectional Data.” Working Paper, UCLA and Stanford. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3214231.

You can install the development version of the package from Github by typing the following commands:

install.packages('devtools', repos = 'http://cran.us.r-project.org') # if not already installed
# devtools::install_github('chadhazlett/kbal')
devtools::install_github("csterbenz1/KBAL", ref = "cat_kernel")
devtools::install_github('xuyiqing/tjbal')

Note that installing kbal (from Github) is required. tjbal also depends on the following packages, which will be installed automatically when tjbal is installed. You can also install them manually:

## for plotting
require(ggplot2)  
## for parallel computing 
require(foreach)  
require(doParallel) 
require(parallel)
## for data manipulation 
require(plyr)

panelView for panel data visualization is also highly recommended:

devtools::install_github('xuyiqing/panelView')

Mac users who have updated to MacOS BigSur or Monterey will likely encounter compilation problems. See here for a potential solution.
Windows users please consider upgrading R to 4.0.0 or higher and installing the latest Rtools to avoid C++17 complier errors when installing fastplm.
For Rcpp, RcppArmadillo and MacOS “-lgfortran” and “-lquadmath” error, click here for details.
Installation failure related to OpenMP on MacOS, click here for a solution.
To fix these issues, try installing gfortran from here.