Title: Calculates SE for Matched Samples from 'MatchIt'
Contains various methods for Standard Error estimation for 'MatchIt' objects.

Title: Combining Matching and Linear Regression for Causal Inference
Description: Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Imports: Hmisc, Matching, graphics, stats

Description: Provides the necessary tools for the pre-processing technique of matching in multiply imputed datasets, to improve the robustness and transparency of deriving causal inferences from studying these datasets. This package includes functions to perform propensity score matching within or across the imputed datasets as well as to estimate weights (including inverse propensity score weights) of observations, to analyze each matched or weighted datasets using parametric or non-parametric statistical models, and to combine the obtained results from these models according to Rubin’s rules. Please see the package repository <https://github.com/FarhadPishgar/MatchThem> for details.
Authors@R: c(person("Farhad", "Pishgar", role = c("aut","cre"), email = "Farhad.Pishgar@Gmail.com"),
person("Clémence", "Leyrat", role = "ctb", email = "Clemence.Leyrat@lshtm.ac.uk"))
Depends: graphics, MatchIt, methods, mice, WeightIt, R (>= 3.5.0)

Title: Matching and Weighting Multiply Imputed Datasets
Description: Provides the necessary tools for the pre-processing techniques of matching and weighting multiply imputed datasets to control for effects of confounders and to reduce the degree of dependence on certain modeling assumptions in studying the causal associations between an exposure and an outcome. This package includes functions to perform matching within and across the multiply imputed datasets using several matching methods, to estimate weights of units in the imputed datasets using several weighting methods, to calculate the causal effect estimate in each matched or weighted dataset using parametric or non-parametric statistical models, and to pool the obtained estimates from these models according to Rubin's rules (please see <https://github.com/FarhadPishgar/MatchThem> for details).
Authors@R: c(person("Farhad", "Pishgar", role = c("aut", "cre"), email = "Farhad.Pishgar@Gmail.com"),
person("Noah", "Greifer", role = "aut", email = "Noah.Greifer@Gmail.com"),
Depends: MatchIt, WeightIt, R (>= 3.5.0)
Imports: dplyr, graphics, methods, mice, stats, survey