Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.

Support package for the textbook "An Introduction to
    Quantitative Text Analysis for Linguists: Reproducible Research using
    R" (Francom, 2024) <doi:10.4324/9781003393764> (available only after August 12, 2024). Includes functions to acquire,
    clean, and analyze text data as well as functions to document and share the
    results of text analysis. The package is designed to be used in conjunction with
    the book, but can also be used as a standalone package for text analysis.

Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.

Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.