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The Journal of Statistical Software publishes articles on statistical software along with the source code of the software itself and replication code for all empirical results.
Site URL: www.jstatsoft.org/index.php/jss
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Posts: 10
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cv: An R Package for Cross-Validating Regression Models
https://www.jstatsoft.org/index.php/jss/article/view/v116i08
Published: June 15, 2026 00:00
We describe the cv package, which implements cross-validation for standard R regression models through a uniform and simple to use generic function. Methods for the cv() function are provided for many commonly employed classes of statistical models,…
BayesMultiMode: Bayesian Mode Inference in R
https://www.jstatsoft.org/index.php/jss/article/view/v116i03
Published: June 5, 2026 00:00
Multimodal univariate distributions arise in many fields such as astrophysics, bioinformatics, climatology and economics due to the heterogeneity of the underlying populations. Mixture processes are a popular tool for accurate approximation of such…
CPU- and GPU-Based Distributed Sampling in Dirichlet Process Mixtures for Large-Scale Analysis
https://www.jstatsoft.org/index.php/jss/article/view/v116i07
Published: May 31, 2026 00:00
In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet process mixture model (DPMM), provide a principled approach for adapting the complexity of the model to the data. Such models are particularly useful…
collapse: Advanced and Fast Statistical Computing and Data Transformation in R
https://www.jstatsoft.org/index.php/jss/article/view/v116i01
Published: May 31, 2026 00:00
collapse is a large C/C++-based infrastructure package facilitating complex statistical computing, data transformation, and exploration tasks in R - at outstanding levels of performance and memory efficiency. It also implements a class-agnostic approach to…
Simulating Complex Cross-Sectional and Longitudinal Data Using the simDAG R Package
https://www.jstatsoft.org/index.php/jss/article/view/v116i02
Published: May 31, 2026 00:00
Generating artificial data is a crucial step when performing Monte Carlo simulation studies. Depending on the planned study, complex data generation processes (DGPs) containing multiple, possibly time-varying, variables with various forms of dependencies…
Policy Learning with the polle Package
https://www.jstatsoft.org/index.php/jss/article/view/v116i04
Published: May 31, 2026 00:00
The R package polle is a unifying framework for learning and evaluating finite stage policies based on observational data. The package implements a collection of existing and novel methods for causal policy learning including doubly robust restricted…
sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields
https://www.jstatsoft.org/index.php/jss/article/view/v115i02
Published: January 31, 2026 00:00
Geostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally…
SMLE: An R Package for Joint Feature Screening in Ultrahigh-Dimensional GLMs
https://www.jstatsoft.org/index.php/jss/article/view/v115i08
Published: January 22, 2026 00:00
Sparsity-restricted maximum likelihood estimation (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features…
singleRcapture: An R Package for Single-Source Capture-Recapture Models
https://www.jstatsoft.org/index.php/jss/article/view/v115i01
Published: January 22, 2026 00:00
Population size estimation is a major challenge in official statistics, social sciences, and natural sciences. The problem can be tackled by applying capture-recapture methods, which vary depending on the number of sources used, particularly on whether a…
TrendLSW: Trend and Spectral Estimation of Nonstationary Time Series in R
https://www.jstatsoft.org/index.php/jss/article/view/v115i10
Published: January 22, 2026 00:00
The TrendLSW R package has been developed to provide users with a suite of wavelet-based techniques to analyze the statistical properties of nonstationary time series. The key components of the package are (a) two approaches for the estimation of the…