Volume 107 Tahun 2023
Dublin Core
Title
Volume 107 Tahun 2023
Source
https://www.jstatsoft.org/issue/view/v107
Date
2023
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Collection Items
Modeling Population Growth in R with the biogrowth Package
The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in
the scientific literature, their application usually requires advanced…
the scientific literature, their application usually requires advanced…
carat: An R Package for Covariate-Adaptive Randomization in Clinical Trials
Covariate-adaptive randomization is gaining popularity in clinical trials because they
enable the generation of balanced allocations with respect to covariates. Over the past
decade, substantial progress has been made in both new innovative…
enable the generation of balanced allocations with respect to covariates. Over the past
decade, substantial progress has been made in both new innovative…
REndo: Internal Instrumental Variables to Address Endogeneity
Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is
violated. The causes of endogeneity are manifold and include response bias in…
violated. The causes of endogeneity are manifold and include response bias in…
DataFrames.jl: Flexible and Fast Tabular Data in Julia
DataFrames.jl is a package written for and in the Julia language offering flexible and
efficient handling of tabular data sets in memory. Thanks to Julia’s unique strengths, it
provides an appealing set of features: Rich support for standard data…
efficient handling of tabular data sets in memory. Thanks to Julia’s unique strengths, it
provides an appealing set of features: Rich support for standard data…
ARCHModels.jl: Estimating ARCH Models in Julia
This paper introduces ARCHModels.jl, a package for the Julia programming language
that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the…
that implements a number of univariate and multivariate autoregressive conditional heteroskedasticity models. This model class is the workhorse tool for modeling the…
varTestnlme: An R Package for Variance Components Testing in Linear and Nonlinear Mixed-Effects Models
The issue of variance components testing arises naturally when building mixed-effects
models, to decide which effects should be modeled as fixed or random or to build parsimonious models. While tests for fixed effects are available in R for models…
models, to decide which effects should be modeled as fixed or random or to build parsimonious models. While tests for fixed effects are available in R for models…
Panel Data Visualization in R (panelView) and Stata (panelview)
We develop an R package panelView and a Stata package panelview for panel data
visualization. They are designed to assist causal analysis with panel data and have three
main functionalities: (1) They plot the treatment status and missing values in…
visualization. They are designed to assist causal analysis with panel data and have three
main functionalities: (1) They plot the treatment status and missing values in…
GMM Estimators for Binary Spatial Models in R
Despite the huge availability of software to estimate cross-sectional spatial models,
there are only few functions to estimate models dealing with spatial limited dependent
variable. This paper fills this gap introducing the new R package spldv.…
there are only few functions to estimate models dealing with spatial limited dependent
variable. This paper fills this gap introducing the new R package spldv.…
Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS
This paper introduces software packages for efficiently imputing missing data using
deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement
a recently developed approach to multiple imputation known as MIDAS, which…
deep learning methods in Python (MIDASpy) and R (rMIDAS). The packages implement
a recently developed approach to multiple imputation known as MIDAS, which…
hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process
The existence of latent clusters with different responses to a treatment is a major
concern in scientific research, as latent effect heterogeneity often emerges due to latent
or unobserved features – e.g., genetic characteristics, personality…
concern in scientific research, as latent effect heterogeneity often emerges due to latent
or unobserved features – e.g., genetic characteristics, personality…