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gamlss-dev

gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape

A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.

Maintained by Mikis Stasinopoulos. Last updated 1 months ago.

4 stars 10.51 score 346 scripts 71 dependents

rebeccasalles

TSPred:Functions for Benchmarking Time Series Prediction

Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Maintained by Rebecca Pontes Salles. Last updated 4 years ago.

benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction

24 stars 5.53 score 94 scripts 1 dependents

valentinian22

BCT:Bayesian Context Trees for Discrete Time Series

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].

Maintained by Valentinian Mihai Lungu. Last updated 3 years ago.

cpp

2.00 score

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