A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
R package innsight interprets deep neural networks predictions.
Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decom…
R package psvmSDR simplifies SDR computation for machine learning.
Optimizes package types for e-commerce to reduce damage and costs.
The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of…
grangersearch tests causal relationships in time series data.
R package sentometrics analyzes text sentiment for predictions.
R package stagedtrees learns staged tree structures from data.
R package huge simplifies graph estimation for high-dimensional data.
Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC packag…
Python package for SPD matrix distances, reproducible and extensible.
Efficient packages solve SLOPE problem in multiple languages.
This paper conducts an empirically study on the trade package composed of a sequence of consecutive purchases or sales of 23 stocks in Chinese stock market. We investigate the probability distributions of the execution time, the number of trades and the total trading volume of trade packages, and analyze the possible s…
Develops rMultiNet R package for multilayer network analysis.
The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems. Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence. TCP gives results wi…
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package to improve their performance via parallel c…
The book "Handbook of Finsler geometry" has been included with a CD containing an elegant Maple package, FINSLER, for calculations in Finsler geometry. Using this package, an example concerning a Finsler generalization of Einstein's vacuum field equations was treated. In this example, the calculation of the components …
L0Learn solves sparse learning problems with millions of features.
Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package ca…
BKP R package models spatially varying binomial probabilities efficiently.
fmeffects package interprets non-linear models in plain language.
DriveML automates machine learning tasks in R.
KnotMosaics package simplifies knot theory computations in SageMath.
Feature Selection (FS) is a key task in Machine Learning. It consists in selecting a number of relevant variables for the model construction or data analysis. We present the R package, FSinR, which implements a variety of widely known filter and wrapper methods, as well as search algorithms. Thus, the package provides …
ctsfeatures R package analyzes categorical time series data.
The Witten deformation associated to a Morse function on a closed Riemannian manifold, via Rellich-Kato theorem, relates analytically the spectral package of the Riemannian manifold (eigenvalues and eigenforms) to the Morse complex defined by the pair (Morse function, Riemannian metric) coupled with the "multivariable …
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
stCEG models spatial events using Chain Event Graphs in R.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
R package for counterfactual explanation methods.
neuralGAM package interprets neural networks by fitting them to feature contributions.
HiPart offers an efficient, interactive tool for hierarchical clustering.
Python package ajdmom simplifies moment formula derivation for jump diffusions.
Python package for estimating intrinsic dimensionality of datasets.
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this …
TML package uses tropical geometry for machine learning tasks.
Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised learning and has seen f…
Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatib…
R package for online forecasting in various fields.
BiDAG R package learns and samples Bayesian network structures efficiently.
latrend simplifies longitudinal clustering for numeric measurements.
Scorio.jl ranks systems from repeated tasks using various methods.
liquidSVM is a package written in C++ that provides SVM-type solvers for various classification and regression tasks. Because of a fully integrated hyper-parameter selection, very carefully implemented solvers, multi-threading and GPU support, and several built-in data decomposition strategies it provides unprecedented…
Python package for functional data analysis.
nn2poly converts neural networks into interpretable polynomial models.
The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the oversampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to …
In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known …
This paper presents the R package GAS for the analysis of time series under the Generalized Autoregressive Score (GAS) framework of Creal et al. (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of nonlinear models. T…