Title: Bayesian emulation, calibration and validation of computer
Description: The SAVE package implements Bayesian statistical
methodology for the analysis of complex computer models.
Namely, it allows for the emulation, calibration, and
validation of computer models, following methodology described

Description: Implements Bayesian statistical methodology for the
It allows for the emulation, calibration, and validation of computer models,
following methodology described in Bayarri et al 2007, Technometrics.

Title: Bayesian Emulation, Calibration and Validation of Computer

Title: Single-Cell RNA-Seq Gene Expression Recovery
Description: 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)
person("Mo", "Huang", email = "mohuangx@gmail.com",
person("Nancy", "Zhang", email = "nzh@wharton.upenn.edu", role = "aut"),
person("Mingyao", "Li", email = "mingyao@pennmedicine.upenn.edu",
Imports: glmnet, foreach, methods, iterators, doParallel, Matrix

Title: Inference and Learning in Stochastic Automata
person("Muhammad Kashif", "Hanif",  role = c("cre","aut"),
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")
Maintainer: Muhammad Kashif Hanif <mkashifhanif@gcuf.edu.pk>
Description: 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>.
Author: Muhammad Kashif Hanif [cre, aut],