Related papers: Efficient implementation of sets and multisets in …
The iotools package provides a set of tools for Input/Output (I/O) intensive datasets processing in R (R Core Team, 2014). Efficent parsing methods are included which minimize copying and avoid the use of intermediate string representations…
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorithms are implemented, as well as various functionalities to…
Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new \hsdar package for R statistical software, which performs a…
This paper describes Resh, a new, statically typed, interpreted programming language and associated runtime for orchestrating multirobot systems. The main features of Resh are: (1) It offloads much of the tedious work of programming such…
A naive realization of JSON data in R maps JSON arrays to an unnamed list, and JSON objects to a named list. However, in practice a list is an awkward, inefficient type to store and manipulate data. Most statistical applications work with…
Motivation: Model selection is a ubiquitous challenge in statistics. For penalized models, model selection typically entails tuning hyperparameters to maximize a measure of fit or minimize out-of-sample prediction error. However, these…
A common problem in many disciplines is the need to assign a set of items into categories or classes with known labels. This is often done by one or more expert raters, or sometimes by an automated process. If these assignments or `ratings'…
This article describes lossless compression algorithms for multisets of sequences, taking advantage of the multiset's unordered structure. Multisets are a generalisation of sets where members are allowed to occur multiple times. A multiset…
We present R package mnlogit for training multinomial logistic regression models, particularly those involving a large number of classes and features. Compared to existing software, mnlogit offers speedups of 10x-50x for modestly sized…
This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM)…
Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially…
One of the most common applications of spatial data analysis is detecting zones, at a certain investigation level, where a point-referenced event under study is especially concentrated. The detection of this kind of zones, which are usually…
With the advent of modern statistical software, complex experimental designs are now routinely employed in many areas of research. Failing to correctly identify the structure of the experimental design can lead to incorrect model selection…
The statistical analysis of structured spatial point process data where the event locations are determined by an underlying spatially embedded relational system has become a vivid field of research. Despite a growing literature on different…
Approximating the set of reachable states of a dynamical system is an algorithmic yet mathematically rigorous way to reason about its safety. Although progress has been made in the development of efficient algorithms for affine dynamical…
In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify…
Everything that exists in R is an object [Chambers2016]. This article examines what would be possible if we kept copies of all R objects that have ever been created. Not only objects but also their properties, meta-data, relations with…
The family of stable distributions received extensive applications in many fields of studies since it incorporates both the skewness and heavy tails. In this paper, we introduce a package written in the R language called alphastable. The…
Scalable ordered maps must ensure that range queries, which operate over many consecutive keys, provide intuitive semantics (e.g., linearizability) without degrading the performance of concurrent insertions and removals. These goals are…
The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this…