stats, RSpectra, utils, Rtsne, graphics, splatter
data.table, batchelor, kBET, scater, knitr, lisi, harmony,
dlfUtils, xtable

Bias Corrected Bootstrap Confidence Intervals
c(person("Bradley", "Efron", role=c("aut"),
email = "brad@stat.stanford.edu"),
person("Balasubramanian", "Narasimhan", role=c("aut", "cre"),
email = "naras@stat.stanford.edu"))
Computation of bootstrap confidence intervals in an almost automatic fashion.
Bradley Efron [aut],
Balasubramanian Narasimhan [aut, cre]

Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).

c(person("Ângelo", "Santanna", role = "aut",
email = "angelo.santanna@ufba.br"),
person("Daniel", "Cerqueira", role = c("aut", "cre"),
email = "danielcerqueira@live.com"),
person("Luca", "Scrucca", role = c("ctb"),
email = "luca.scrucca@unipg.it"))
Applies beta control charts to defined values, using 'qcc' package with new beta control limits. The Beta Chart presents the control limits based on the Beta probability distribution. Can be used for monitoring fraction data from Binomial distribution as replacement of the p-Charts. The Beta Chart was applied for monitoring the variables in three real studies, and it was compared to the control limits with three schemes. The comparative analysis showed that: (i) Beta approximation to the Binomial distribution was more appropriate with values confined in the [0, 1]- interval; and (ii) the charts proposed were more sensitive to the average run length (ARL), in both in-control and out-of-control processes monitoring. The Beta Charts outperform the Shewhart control charts analyzed for monitoring fraction data. Ângelo Márcio Oliveira Sant’Anna, Carla Schwengber ten Caten (2012) <doi:10.1016/j.eswa.2012.02.146>.
Ângelo Santanna [aut],
Daniel Cerqueira [aut, cre],
Luca Scrucca [ctb]

Developed for the following tasks. 1) Computing the Fisher information matrix under
progressive type I interval censoring scheme, 2) Computing the bias corrected maximum
likelihood estimator under progressive type I interval censoring based on the method of Cox
and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> , 3) Computing the goodness of fit statistics including Anderson Darling and Cramer-von Mises , and 4) Simulating
the progressive type I interval censoring scheme.