Volume 105 Tahun 2023
Dublin Core
Title
Volume 105 Tahun 2023
Source
https://www.jstatsoft.org/issue/view/v105
Date
2023
Contributor
Fajar Bagus W
Format
PDF
Language
English
Type
Text
Collection Items
cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
Sparse graphical models have revolutionized multivariate inference. With the advent
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse…
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse…
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination
of additive regression models and deep networks. Our implementation…
of additive regression models and deep networks. Our implementation…
spsurvey: Spatial Sampling Design and Analysis in R
spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts()…
umpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets
We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute
a set of non-parametric estimators of all contributions composing a…
a set of non-parametric estimators of all contributions composing a…
Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg
Recurrent event analyses have found a wide range of applications in biomedicine, public
health, and engineering, among others, where study subjects may experience a sequence of
event of interest during follow-up. The R package reReg offers a…
health, and engineering, among others, where study subjects may experience a sequence of
event of interest during follow-up. The R package reReg offers a…
ergm 4: New Features for Analyzing Exponential-Family Random Graph Models
The ergm package supports the statistical analysis and simulation of network data. It
anchors the statnet suite of packages for network analysis in R introduced in a special issue
in Journal of Statistical Software in 2008. This article provides an…
anchors the statnet suite of packages for network analysis in R introduced in a special issue
in Journal of Statistical Software in 2008. This article provides an…
Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations
Despite the large body of research on missing value distributions and imputation, there
is comparatively little literature with a focus on how to make it easy to handle, explore,
and impute missing values in data. This paper addresses this gap. The…
is comparatively little literature with a focus on how to make it easy to handle, explore,
and impute missing values in data. This paper addresses this gap. The…
Additive Bayesian Network Modeling with the R Package abn
The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or
information theoretic formulations of generalized linear models. It is equipped…
information theoretic formulations of generalized linear models. It is equipped…
Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for
structure learning and sampling of Bayesian networks. The package includes tools to
search for a maximum a posteriori (MAP) graph and to sample graphs from the…
structure learning and sampling of Bayesian networks. The package includes tools to
search for a maximum a posteriori (MAP) graph and to sample graphs from the…
logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations
This paper introduces the logitr R package for fast maximum likelihood estimation of
multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals…
multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals…