c(person(given="Jesus",family="Palomo", role=c("aut",
"cre"),email="jesus.palomo@urjc.es"),
person(given="Gonzalo",family="Garcia-Donato",
role="aut",email="gonzalo.garciadonato@uclm.es"),
person(given="Rui",family="Paulo", role="aut",email="rui@iseg.utl.pt"),
person(given="James",family="Berger",
role="ctb",email="berger@stat.duke.edu"),
person(given="Maria Jesus",family="Bayarri",
role="ctb",email="susie.bayarri@uv.es"),
person(given="Jerome",family="Sacks",
role="ctb",email="sacks@niss.org"))
Implements Bayesian statistical methodology for the
analysis of complex computer models.
It allows for the emulation, calibration, and validation of computer models,
following methodology described in Bayarri et al 2007, Technometrics.
Jesus Palomo [aut, cre],
Gonzalo Garcia-Donato [aut],
Rui Paulo [aut],
James Berger [ctb],
Maria Jesus Bayarri [ctb],
Jerome Sacks [ctb]

Bayesian Emulation, Calibration and Validation of Computer
Models

Single-Cell RNA-Seq Gene Expression Recovery
An implementation of a regularized regression prediction and
empirical Bayes method to recover the true gene expression profile in
noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018)
<doi:10.1038/s41592-018-0033-z> for more details.
c(
person("Mo", "Huang", email = "mohuangx@gmail.com",
role = c("aut", "cre")),
person("Nancy", "Zhang", email = "nzh@wharton.upenn.edu", role = "aut"),
person("Mingyao", "Li", email = "mingyao@pennmedicine.upenn.edu",
role = "aut")
)
glmnet, foreach, methods, iterators, doParallel, Matrix
Mo Huang [aut, cre],
Nancy Zhang [aut],
Mingyao Li [aut]

Inference and Learning in Stochastic Automata
c(
person("Muhammad Kashif", "Hanif",  role = c("cre","aut"),
email = "mkashifhanif@gcuf.edu.pk"),
person("Muhammad Umer", "Sarwar", role = "aut", email = "Mumersarwar@gcuf.edu.pk"),
person("Rehman", "Ahmad", role = "aut", email = "rehman.ahmad777@gmail.com"),
person("Zeeshan", "Ahmad", role = "aut", email = "zeeshan.cs822@gmail.com "),
person("Karl-Heinz", "Zimmermann", role = "aut", email = "k.zimmermann@tuhh.de")
)
Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
Muhammad Kashif Hanif [cre, aut],
Muhammad Umer Sarwar [aut],
Rehman Ahmad [aut],
Zeeshan Ahmad [aut],
Karl-Heinz Zimmermann [aut]