Description: Implements the method of Hofmeyr, D.P. (2019) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.

Description: Implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.

Title: A Fast Large-Scale Almost Matching Exactly Approach to Causal
Authors@R: c(person("Chia-Rui (Jerry)", "Chang", role=c("aut", "cre"),
person("Cynthia", "Rudin", role = c("aut"),
person("Alexander", "Volfovsky", role = c("aut"),
person("Sudeepa", "Roy", role = c("aut"),
person("Tianyu", "Wang", role = c("ctb"),
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
Maintainer: Chia-Rui (Jerry) Chang <chiarui424@gmail.com>
Imports: reticulate (>= 1.9), graphics, stats, latticeExtra, dplyr,
RPostgreSQL, RSQLite, gmp, lattice, rlang
Suggests: knitr, rmarkdown, ggplot2, ggpubr, e1071
Author: Chia-Rui (Jerry) Chang [aut, cre],