DESCRIPTION

Package: FLAME
Type: Package
Title: A Fast Large-Scale Almost Matching Exactly Approach to Causal Inference
Version: 1.1.0
Authors@R: c(person("Chia-Rui (Jerry)", "Chang", role=c("aut", "cre"),
    email = "chiarui_chang@g.harvard.edu"),
    person("Cynthia", "Rudin", role = c("aut"),
    email = "cynthia@cs.duke.edu"),
    person("Alexander", "Volfovsky", role = c("aut"),
    email = "alexander.volfovsky@duke.edu"),
    person("Sudeepa", "Roy", role = c("aut"),
    email = "sudeepa@cs.duke.edu"),
    person("Tianyu", "Wang", role = c("ctb"),
    email = "tianyu@cs.duke.edu"))
Description: The 'FLAME' (Fast Large-scale Almost Matching Exactly) package implements the
    matching algorithm in Roy, Rudin, Volfovsky, and Wang (2017) <arXiv:1707.06315>. 'FLAME' 
    performs matching of treatment and control units in the potential outcomes framework 
    for large categorical datasets. 'FLAME' creates matches that include as many covariates 
    as possible, and sequentially drops covariates that are less useful based on a match 
    quality measure. Match quality combines two important elements – it considers predictive 
    power from machine learning on a hold out training set, and a balancing factor to ensure 
    that it does not remove a covariate that would ruin overlap between treatment and 
    control groups. Currently the 'FLAME' package applies to categorical data, and 
    provides two approaches for implementation - bit vectors and database management systems 
    (e.g., 'PostgreSQL', 'SQLite'). For data that has been adequately processed and fits in memory, 
    bit vectors should be applied. For extremely large data that does not fit in memory, database 
    systems should be applied.
Maintainer: Chia-Rui (Jerry) Chang <chiarui_chang@g.harvard.edu>
URL: https://github.com/JerryChiaRuiChang/FLAME
BugReports: https://github.com/JerryChiaRuiChang/FLAME/issues
Depends:
    R (>= 3.3)
Imports:
    graphics,
    stats,
    latticeExtra,
    dplyr,
    RPostgreSQL,
    RSQLite,
    gmp,
    lattice,
    rlang
Suggests: 
    knitr,
    rmarkdown,
    ggplot2,
    ggpubr,
    e1071
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1