Predicts individual race/ethnicity using surname, first name,
    middle name, geolocation, and other attributes, such as gender and
    age. The method utilizes Bayes' Rule (with optional measurement error
    correction) to compute the posterior probability of each racial
    category for any given individual. The package implements methods
    described in Imai and Khanna (2016) "Improving Ecological Inference by
    Predicting Individual Ethnicity from Voter Registration Records"
    Political Analysis <DOI:10.1093/pan/mpw001> and Imai, Olivella, and
    Rosenman (2022) "Addressing census data problems in race imputation
    via fully Bayesian Improved Surname Geocoding and name supplements"
    <DOI:10.1126/sciadv.adc9824>.  The package also incorporates the data
    described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity
    data for first, middle, and surnames"
    <DOI:10.1038/s41597-023-02202-2>.

Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

This dataset was collected using a new four-arm within-study comparison design. The study aimed to examine the impact of a mathematics training intervention and a vocabulary study session on post-test scores in mathematics and vocabulary, respectively. The innovative four-arm within-study comparison design facilitates both experimental and quasi-experimental identification of average causal effects.

Easily override the default visual choices in 'ggplot2' to make 
    your time series plots look more like the Wall Street Journal. Specific 
    theme design choices include omitting x-axis grid lines and displaying 
    sparse light grey y-axis grid lines. Additionally, this allows to label 
    the y-axis scales with your units only displayed on the top-most number, 
    while also removing the bottom most number (unless specifically 
    overridden). The goal is visual simplicity, because who has time to waste 
    looking at a cluttered graph?