Browse Items (9431 total)

spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts()…

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination
of additive regression models and deep networks. Our implementation…

Sparse graphical models have revolutionized multivariate inference. With the advent
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse…

Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational econometric specification to simultaneously incorporate spatial effects and correlated error terms across equations. The purpose of the spsur R package is to…

The use of mixed frequency data is now common in many applications, ranging from
the analysis of high frequency financial time series to large cross-sections of macroeconomic
time series. In this article, we show how state space methods can easily…

This document introduces the R package BGVAR to estimate Bayesian global vector
autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian
treatment of GVARs allows to include large information sets by mitigating issues…

The highfrequency package for the R programming language provides functionality
for pre-processing financial high-frequency data, analyzing intraday stock returns, and
forecasting stock market volatility. For academics and practitioners alike, it…

synthACS is an R package that provides flexible tools for building synthetic microdatasets based on American Community Survey (ACS) base tables, allows data-extensibility
and enables to conduct spatial microsimulation modeling (SMSM) via simulated…

A large body of research has focused on theory and computation for variable selection
techniques for high dimensional data. There has been substantially less work in the
big “tall” data paradigm, where the number of variables may be large, but the…

The R package calculus implements C++-optimized functions for numerical and symbolic calculus, such as the Einstein summing convention, fast computation of the LeviCivita symbol and generalized Kronecker delta, Taylor series expansion, multivariate…