Algorithms for Linear Time Series Analysis: With R Package

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software.

Algorithms for Linear Time Series Analysis: With R Package

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software

Upper Palaeolithic genomes reveal deep roots of modern Eurasians.

We extend the scope of European palaeogenomics by sequencing the genomes of Late Upper Palaeolithic (13,300 years old, 1.4-fold coverage) and Mesolithic (9,700 years old, 15.4-fold) males from western Georgia in the Caucasus and a Late Upper Palaeolithic (13,700 years old, 9.5-fold) male from Switzerland. While we detect Late Palaeolithic-Mesolithic genomic continuity in both regions, we find that Caucasus hunter-gatherers (CHG) belong to a distinct ancient clade that split from western hunter-gatherers ∼45 kya, shortly after the expansion of anatomically modern humans into Europe and from the ancestors of Neolithic farmers ∼25 kya, around the Last Glacial Maximum. CHG genomes significantly contributed to the Yamnaya steppe herders who migrated into Europe ∼3,000 BC, supporting a formative Caucasus influence on this important Early Bronze age culture. CHG left their imprint on modern populations from the Caucasus and also central and south Asia possibly marking the arrival of Indo-Aryan languages.This research was supported by the European Research Council (ERC) Starting Grant to R.P. (ERC-2010-StG 263441). D.B., M.H and AM. were also supported by the ERC (295729-CodeX, 310763-GeneFlow and 647787-LocalAdaptation respectively). The National Geographic Global Exploration Fund funded fieldwork in Satsurblia Cave l from April 2013 to February 2014 (grant- GEFNE78–13). V.S. was supported by a scholarship from the Gates Cambridge Trust and M.G.L. by a BBSRC DTP studentship. C.G. was supported by the Irish Research Council for Humanities and Social Sciences (IRCHSS) ERC Support Programme and the Marie-Curie Intra-European Fellowships (FP7-IEF-328024). R.M. was funded by the BEAN project of the Marie Curie ITN (289966) and L.C. by the Irish Research Council (GOIPG/2013/1219). R.L.M. was funded by the ALS Association of America (2284) and Fondation Thierry Latran (ALSIBD). M.C. was supported by Swiss NSF grant 31003A_156853. We acknowledge Shota Rusataveli Georgian National Science Foundation as well as the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and Science Foundation Ireland (12/ERC/B2227) for provision of sequencing facilities. We thank Valeria Mattiangeli and Matthew D. Teasdale for their assistance.This is the final version of the article. It was first available from NPG via http://dx.doi.org/10.1038/ncomms991

Periodic Poisson Processes and Almost-lack-of-memory Distributions

Certain characterization properties of time-varying periodic Poisson flows are studiedin terms of almost-lack-of-memory (ALM) distributions. Parameter estimation formulas arederived. A method for verifying the hypothesis on the membership of a sample to the classof ALM-distributions is developed. Algorithms for computing critical levels and power of thelikelihood ratio test by the Monte Carlo method are designed