This article introduces the spxtivdfreg package in Stata, which implements a general instrumental variables (IV) approach for estimating dynamic spatial panel data models with unobserved common factors or interactive effects, when the number of both cross-sectional and time series observations is large. The estimator has been developed in a recent paper by Cui, Sarafidis, and Yamagata (2023). The underlying idea is to project out the common factors from exogenous covariates using principal components analysis, and to run IV regression in both of two stages, using defactored covariates (and their spatial counterparts) as instruments. The resulting two-stage IV estimator is valid for models with homogeneous slope coefficients, and has several advantages relative to existing popular approaches. In addition, the spxtivdfreg package allows estimation of short-run and long-run direct and indirect effects, as well as total effects, accounting for the cumulative effects over time and across space. Standard errors for such effects are computed using the Delta method. Last, the spxtivdfreg package allows for heterogeneous slope coefficients, as in Chen, Cui, Sarafidis, and Yamagata (2025). In particular, we construct a

In statistical modeling, there is a wide variety of generalized linear models for categorical response variables (nominal or ordinal responses); yet, there is no software embracing all these models together in a unique and generic framework. We propose and present GLMcat, an R package to estimate generalized linear models implemented under the unified specification (r, F, Z) where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative distribution function for the linkage, and Z the design matrix. All classical models (and their variations) for categorical data can be written as an (r, F, Z) triplet, thus, they can be fitted with GLMcat. The functions in the package are intuitive and user-friendly. For each of the three components, there are multiple alternatives from which the user should thoroughly select those that best address the objectives of the analysis. The main strengths of the GLMcat package are the possibility of choosing from a large number of link functions (defined by the composition of F and r) and the simplicity for setting constraints in the linear prediction, either on the intercepts or on the slopes. This paper proposes a methodological and practical guide for the appropriate selection of a model considering the concordance between the nature of the data and the properties of the model.

Partitioning a data set by one or more of its attributes and computing an aggregate for each part is one of the most common operations in data analyses. There are use cases where the partitioning is determined dynamically by collapsing smaller subsets into larger ones, to ensure sufficient support for the computed aggregate. These use cases are not supported by software implementing split-apply-combine types of operations. This paper presents the R package accumulate that offers convenient interfaces for defining grouped aggregation where the grouping itself is dynamically determined, based on user-defined conditions on subsets, and a user-defined subset collapsing scheme. The formal underlying algorithm is described and analyzed as well.

With the rapid development of sequencing technology, the costs of individual genotyping have been reduced dramatically, leading to genomic prediction and genome-wide association studies being widely promoted and used to predict the unknown phenotypes and to locate candidate genes for animal and plant economic traits and, increasingly, for human diseases. Developing new advanced statistical models to improve prediction accuracy and location precision for the traits with various genetic architectures has always been a hot topic in those two research domains. The Bayesian regression model (BRM) has played a crucial role in the past decade, and it has been used widely in relevant genetic analyses owing to its flexible model assumptions on the unknown genetic architecture of complex traits. To fully utilize the available data from either a self-designed experimental population or a public database, statistical geneticists have constantly extended the fitting capacity of BRM, and a series of new methodologies have been proposed for different application scenarios. Here we introduce the R package hibayes, a software tool that can be used to fit individual-level, summary-level, and single-step Bayesian regression models. Including also the richest methods achieved thus far, it covers most of the functionalities involved in the field of genomic prediction and genome-wide association studies, potentially helping to address a wide range of research problems, while retaining an easy-to-learn and flexible-to-use experience. We believe that package hibayes will facilitate the academic research and practical application of statistical genetics for humans, plants, and animals.

Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph; second, a fast, exact method for regularly spaced data modeled by GPs with stationary kernels using the fast Fourier transform. Based on benchmark experiments, we offer guidance for practitioners to decide between different methods and parameterizations. We consider two real-world examples to illustrate the package. The implementation follows Stan's design and exposes performant inference through a familiar interface. Full posterior inference for ten thousand data points is feasible on a laptop in less than 20 seconds. Details on how to get started using the popular interfaces cmdstanpy for Python and cmdstanr for R are provided.

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, q, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of d < q latent variables. By adding reduced-rank functionality to the package glmmTMB, we enhance the mixed models available to include random effects of dimensions that were previously not possible. We apply the reduced-rank random effect to two examples, estimating a generalized latent variable model for multivariate abundance data and a random-slopes model.

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 one-inflated beta and flexible beta ones, possibly accounting for spatial and/or temporal dependency structures. The models, developed within a Bayesian framework, are estimated through Stan language, allowing fast estimation and customized parallel computing. The additional features of the tipsae package, such as diagnostics, visualization and exporting functions as well as variance smoothing and benchmarking functions, improve the user experience through the entire process of estimation, validation and outcome presentation. A shiny application with a user-friendly interface further eases the implementation of Bayesian models for small area analysis.

The Extremes.jl package provides exhaustive, high-performance functions by leveraging the multiple-dispatch capabilities in Julia for the analysis of extreme values. In particular, the package implements statistical models for both block maxima and peaksover-threshold methods, along with several methods for the generalized extreme value and generalized Pareto distributions used in extreme value theory. Additionally, the package offers various parameter estimation methods, such as probability-weighted moments, maximum likelihood, and Bayesian estimation. It also includes tools for handling dependence in excesses over a threshold and methods for managing nonstationary models. Inference for extreme quantiles is available for both stationary and nonstationary models, along with diagnostic figures to assess the goodness of fit of the model to the data.

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 software packages, we make use of analytical derivatives implemented in efficient C++ code for nonlinear log-likelihood optimization. This allows fast parameter estimation even in higher model dimensions N > 3. The baseline BEKK model is complemented with an asymmetric parameterization that allows for a flexible modeling of conditional (co)variances. Furthermore, we provide the user with the simplified scalar and diagonal BEKK models to deal with high dimensionality of heteroskedastic time series. The package is designed in an object-oriented way featuring a comprehensive toolbox of methods to investigate and interpret, for instance, volatility impulse response functions, risk estimation and forecasting (VaR) and a backtesting algorithm to compare the forecasting performance of alternative BEKK models. For illustrative purposes, we analyze a bivariate ETF return series (S&P, US treasury bonds) and a four-dimensional system comprising, in addition, a gold ETF and changes of a log oil price by means of the suggested package. We find that the BEKKs package is more than 100 times faster for time series systems of dimension N > 3 than other existing packages.