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Kristján Jónasson

Publications and source records attributed to Kristján Jónasson.

2 recordsLinked to original sources

Burn-in-Free Simulation of VARMA Time Series

Varmapack is a software package for efficient, exact simulation of VARMA time series without a burn-in period. For stationary models, Varmapack can generate initial states and innovations from their joint stationary distribution. Alternatively, the user can supply initial states, in which case innovations are generated from their conditional distribution. The core C library has interfaces to R, Python, and Matlab. It also offers VARMAX simulation and computation of autocovariances and correlations, spectral radii, and impulse responses. Varmapack automatically selects between vector-Yule-Walker and state-space methods for covariance computation and uses level-3 BLAS operations for efficient generation of multiple replicates. Benchmarks on several platforms show substantial performance gains over existing simulation software, ranging from several-fold to more than three orders of magnitude for the packages and models considered. Varmapack is open source and publicly available through GitHub.

stat.AP↗

Randompack: Cross-Platform Reproducible Random Number Generation and Distribution Sampling

A C library for random number generation, Randompack, is presented. The library implements several modern random number generators (engines), including xoshiro256, PCG64, Philox, ranlux++, and sfc64; 14 continuous distributions including uniform, normal, exponential, gamma, beta, and multivariate normal; raw bit streams, bounded integers, permutations, and sampling without replacement. The engine and the distribution layers are separated so any engine can be used with any distribution. Benchmarks show that Randompack is faster overall than competing libraries, with speedup factors ranging from about 1 to 15 depending on engine, distribution, interface, and platform. A distinguishing feature is reproducibility: with the same seeds Randompack gives compatible results across programming languages, computers, CPU architectures, and compilers. The library includes comprehensive support for parallel simulation. It is accompanied by a comprehensive test suite, benchmarking programs, and example programs. Interfaces to Fortran, Python, Julia, and R have been implemented; their benchmark results are included, although their design and implementation are otherwise outside the scope of the article. Unlike other available C libraries with comparable scope, Randompack is permissively licensed under the MIT license, and it is open source and publicly available through GitHub and conda-forge.

stat.AP↗