c(
person("Amrei", "Stammann", email = "amrei.stammann@rub.de", role = c("aut", "cre")),
person("Daniel", "Czarnowske", email = "daniel.czarnowske@hhu.de", role = c("aut"),
comment = c(ORCID = "0000-0002-0030-929X"))
)
Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.

Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, doi: 10.1080/01621459.1993.10476339 for details.

Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.

Algorithms for High-Throughput Sequencing of Antigen-Specific T
Cells
person("Edward", "Lee", email = "email.edwardlee@gmail.com", role = c("aut", "cre"))
Provides algorithms for frequency-based pairing of alpha-beta T
cell receptors.

Confidence Intervals for Coefficient Alpha and Standardized
Alpha
person("Jonas", "Moss", , "jonas.moss.statistics@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6876-6964"))
Calculate confidence intervals for alpha and standardized
alpha using asymptotic theory or the studentized bootstrap, with or
without transformations. Supports the asymptotic distribution-free
method of Maydeu-Olivares, et al. (2007)
<doi:10.1037/1082-989X.12.2.157>, the pseudo-elliptical method of Yuan
& Bentler (2002) <doi:10.1007/BF02294845>, and the normal method of
van Zyl et al. (1999) <doi:10.1007/BF02296146>, for both coefficient
alpha and standardized alpha.
covr, extraDistr, knitr, lavaan, psychTools, rmarkdown,
testthat (>= 3.0.0)