R-universe search: exports:RW

Showing 10 of total 10 results (show query)

tidyverts

fable:Forecasting Models for Tidy Time Series

Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Maintained by Mitchell OHara-Wild. Last updated 4 months ago.

forecasting cpp

569 stars 13.54 score 2.1k scripts 6 dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 18 hours ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

148 stars 9.92 score 117 scripts

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

15 stars 7.68 score 59 scripts 4 dependents

bayesiandemography

bage:Bayesian Estimation and Forecasting of Age-Specific Rates

Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.

Maintained by John Bryant. Last updated 13 days ago.

cpp

3 stars 7.41 score 39 scripts

ousuga

RelDists:Estimation for some Reliability Distributions

Parameters estimation and linear regression models for Reliability distributions families reviewed by Almalki & Nadarajah (2014) <doi:10.1016/j.ress.2013.11.010> using Generalized Additive Models for Location, Scale and Shape, GAMLSS by Rigby & Stasinopoulos (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.

Maintained by Freddy Hernandez-Barajas. Last updated 4 days ago.

4 stars 6.18 score 19 scripts

calbertsen

argosTrack:Fit Movement Models to Argos Data for Marine Animals

Fits various state-space animal movement models to Argos data. Models are fitted by maximum likelihood with the Laplace approximation via 'TMB' and 'nlminb'. Functionality to visualize and simulate the models is available.

Maintained by Christoffer Moesgaard Albertsen. Last updated 4 years ago.

animal animal-behavior argos-data c-plus-plus gps-data movement movement-ecology solar-geolocator tracking cpp

12 stars 3.92 score 14 scripts

gvalentini58

RANKS:Ranking of Nodes with Kernelized Score Functions

Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.

Maintained by Giorgio Valentini. Last updated 3 years ago.

1.90 score 7 scripts

cran

symmetry:Testing for Symmetry of Data and Model Residuals

Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All tests are implemented using the 'Rcpp' package which ensures great performance of the code.

Maintained by Blagoje Ivanović. Last updated 2 years ago.

openblas cpp

1.06 score

cran

RobustANOVA:Robust One-Way ANOVA Tests under Heteroscedasticity and Nonnormality

Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.

Maintained by Gamze Guven. Last updated 2 years ago.

1.00 score

cran

RobustBF:Robust Solution to the Behrens-Fisher Problem

Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.

Maintained by Gamze Guven. Last updated 3 years ago.

1.00 score