volume 111 Tahun 2024
Dublin Core
Title
volume 111 Tahun 2024
Source
https://www.jstatsoft.org/issue/view/v111
Date
2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Collection Items
clinicalsignificance: Clinical Significance Analyses of Intervention Studies in R
The analysis of clinical significance is helpful to decide if an intervention leads to practically relevant or meaningful changes for individual patients which is clearly different
from the analysis of statistical significance. However, the…
from the analysis of statistical significance. However, the…
jti and sparta: Time and Space Efficient Packages for Model-Based Prediction in Large Bayesian Networks
A Bayesian network is a multivariate (potentially very high dimensional) probabilistic
model formed by combining lower-dimensional components. In Bayesian networks, the
computation of conditional probabilities is fundamental for model-based…
model formed by combining lower-dimensional components. In Bayesian networks, the
computation of conditional probabilities is fundamental for model-based…
GET: Global Envelopes in R
This work describes the R package GET that implements global envelopes for a general
set of d-dimensional vectors T in various applications. A 100(1−α)% global envelope is a
band bounded by two vectors such that the probability that T falls outside…
set of d-dimensional vectors T in various applications. A 100(1−α)% global envelope is a
band bounded by two vectors such that the probability that T falls outside…
BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series
We describe the R package BEKKs, which implements the estimation and diagnostic
analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing…
analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing…
Birth-and-Death Processes in Python: The BirDePy Package
Birth-and-death processes (BDPs) form a class of continuous-time Markov chains that
are particularly suited to describing the changes in the size of a population over time.
Population-size-dependent BDPs (PSDBDPs) allow the rate at which a…
are particularly suited to describing the changes in the size of a population over time.
Population-size-dependent BDPs (PSDBDPs) allow the rate at which a…
pyStoNED: A Python Package for Convex Regression and Frontier Estimation
Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields. In the field of productivity
and efficiency analysis, recent developments in multivariate convex…
and efficiency analysis, recent developments in multivariate convex…
mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R
Spatial and spatiotemporal machine-learning models require a suitable framework for
their model assessment, model selection, and hyperparameter tuning, in order to avoid
error estimation bias and over-fitting. This contribution provides an overview…
their model assessment, model selection, and hyperparameter tuning, in order to avoid
error estimation bias and over-fitting. This contribution provides an overview…
Interpreting Deep Neural Networks with the Package innsight
The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks’ predictions with so-called feature attribution methods.
Aside from the unified and user-friendly framework, the package stands out…
Aside from the unified and user-friendly framework, the package stands out…
How to Interpret Statistical Models Using marginaleffects for R and Python
The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or
transformations. Analysts who fit such complex models often seek to transform raw…
transformations. Analysts who fit such complex models often seek to transform raw…
Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo
Contemporary empirical applications frequently require flexible regression models for
complex response types and large tabular or non-tabular, including image or text, data.
Classical regression models either break down under the computational load…
complex response types and large tabular or non-tabular, including image or text, data.
Classical regression models either break down under the computational load…