Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].

Likelihood-based inference methods with doubly-truncated data are developed under various models.
 Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and
 Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>.
 Parametric models from the special exponential family (SEF) are based on
 Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and
 Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>.
 The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.

Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.

Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.

Implementation of the double/debiased machine learning framework of
    Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
    regression models, partially linear instrumental variable regression models,
    interactive regression models and interactive instrumental variable
    regression models. 'DoubleML' allows estimation of the nuisance parts in
    these models by machine learning methods and computation of the Neyman
    orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
    'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
    the 'R6' package is very flexible. More information available in the
    publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.