Volume 92 Tahun 2020
Dublin Core
Title
Volume 92 Tahun 2020
Source
https://www.jstatsoft.org/issue/view/v092
Collection Items
Most Likely Transformations: The mlt Package
The mlt package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterization of the unconditional or conditional transformation function using infrastructure from package…
The Calculus of M-Estimation in R with geex
M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find
roots and compute the empirical sandwich variance estimator for any set of…
roots and compute the empirical sandwich variance estimator for any set of…
PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Many real-world systems are profitably described as complex networks that grow over
time. Preferential attachment and node fitness are two simple growth mechanisms that
not only explain certain structural properties commonly observed in real-world…
time. Preferential attachment and node fitness are two simple growth mechanisms that
not only explain certain structural properties commonly observed in real-world…
Integration of R and Scala Using rscala
The rscala software is a simple, two-way bridge between R and Scala that allows users
to leverage the unique strengths of both languages in a single project. Scala classes can
be instantiated from R and Scala methods can be called. Arbitrary Scala…
to leverage the unique strengths of both languages in a single project. Scala classes can
be instantiated from R and Scala methods can be called. Arbitrary Scala…
BPEC: An R Package for Bayesian Phylogeographic and Ecological Clustering
BPEC is an R package for Bayesian phylogeographic and ecological clustering which
allows geographical, environmental and phenotypic measurements to be combined with
deoxyribonucleic acid (DNA) sequences in order to reveal geographic structuring of…
allows geographical, environmental and phenotypic measurements to be combined with
deoxyribonucleic acid (DNA) sequences in order to reveal geographic structuring of…
The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent
a widely used class of population-based metaheuristics for the solution of multicriteria
optimization problems. We introduce the MOEADr package, which offers many of…
a widely used class of population-based metaheuristics for the solution of multicriteria
optimization problems. We introduce the MOEADr package, which offers many of…
SQUAREM: An R Package for Off-the-Shelf Acceleration of EM, MM and Other EM-Like Monotone Algorithms
We discuss the R package SQUAREM for accelerating iterative algorithms which exhibit slow, monotone convergence. These include the well-known expectation-maximization
algorithm, majorize-minimize (MM), and other EM-like algorithms such as…
algorithm, majorize-minimize (MM), and other EM-like algorithms such as…
Computing the Oja Median in R: The Package OjaNP
The Oja median is one of several extensions of the univariate median to the multivariate case. It has many desirable properties, but is computationally demanding. In this
paper, we first review the properties of the Oja median and compare it to…
paper, we first review the properties of the Oja median and compare it to…
spBayesSurv: Fitting Bayesian Spatial Survival Models Using R
Spatial survival analysis has received a great deal of attention over the last 20 years
due to the important role that geographical information can play in predicting survival.
This paper provides an introduction to a set of programs for…
due to the important role that geographical information can play in predicting survival.
This paper provides an introduction to a set of programs for…
bridgesampling: An R Package for Estimating Normalizing Constants
Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These
normalizing constants are notoriously difficult to obtain, as they usually…
normalizing constants are notoriously difficult to obtain, as they usually…