Volume 108 Tahun 2024
Dublin Core
Title
Volume 108 Tahun 2024
Source
https://www.jstatsoft.org/issue/view/v108
Date
2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Collection Items
The R Package tipsae: Tools for Mapping Proportions and Indicators on the Unit Interval
The tipsae package implements a set of small area estimation tools for mapping proportions and indicators defined on the unit interval. It provides for small area models
defined at area level, including the classical beta regression, zero- and/or…
defined at area level, including the classical beta regression, zero- and/or…
The R Package markets: Estimation Methods for Markets in Equilibrium and Disequilibrium
Market models constitute a significant cornerstone of empirical applications in business, industrial organization, and policymaking macroeconomics. The econometric literature proposes various estimation methods for markets in equilibrium, which…
DoubleML: An Object-Oriented Implementation of Double Machine Learning in R
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
It provides functionalities to estimate parameters in causal models based on machine…
It provides functionalities to estimate parameters in causal models based on machine…
gcimpute: A Package for Missing Data Imputation
This article introduces the Python package gcimpute for missing data imputation.
Package gcimpute can impute missing data with many different variable types, including
continuous, binary, ordinal, count, and truncated values, by modeling data as…
Package gcimpute can impute missing data with many different variable types, including
continuous, binary, ordinal, count, and truncated values, by modeling data as…
melt: Multiple Empirical Likelihood Tests in R
Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models. Empirical likelihoodbased tests are asymptotically pivotal and thus avoid explicit studentization.…
PUMP: Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-Level Experiments
For randomized controlled trials (RCTs) with a single intervention’s impact being measured on multiple outcomes, researchers often apply a multiple testing procedure (such
as Bonferroni or Benjamini-Hochberg) to adjust p values. Such an adjustment…
as Bonferroni or Benjamini-Hochberg) to adjust p values. Such an adjustment…
Holistic Generalized Linear Models
Holistic linear regression extends the classical best subset selection problem by adding
additional constraints designed to improve the model quality. These constraints include
sparsity-inducing constraints, sign-coherence constraints and linear…
additional constraints designed to improve the model quality. These constraints include
sparsity-inducing constraints, sign-coherence constraints and linear…
salmon: A Symbolic Linear Regression Package for Python
One of the most attractive features of R is its linear modeling capabilities. We describe
a Python package, salmon, that brings the best of R’s linear modeling functionality to
Python in a Pythonic way – by providing composable objects for…
a Python package, salmon, that brings the best of R’s linear modeling functionality to
Python in a Pythonic way – by providing composable objects for…
Modeling Nonstationary Financial Volatility with
the R Package tvgarch
Certain events can make the structure of volatility of financial returns to change,
making it nonstationary. Models of time-varying conditional variance such as generalized
autoregressive conditional heteroscedasticity (GARCH) models usually assume…
making it nonstationary. Models of time-varying conditional variance such as generalized
autoregressive conditional heteroscedasticity (GARCH) models usually assume…
Modeling Big, Heterogeneous, Non-Gaussian
Spatial and Spatio-Temporal Data Using FRK
Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent,
and their analysis is needed in a variety of disciplines. FRK is an R package for spatial
and spatio-temporal modeling and prediction with very large data sets that,…
and their analysis is needed in a variety of disciplines. FRK is an R package for spatial
and spatio-temporal modeling and prediction with very large data sets that,…