Showing 51 of total 51 results (show query)
bsvars
bsvars:Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.
Maintained by Tomasz Woźniak. Last updated 1 months ago.
bayesian-inferenceeconometricsvector-autoregressionopenblascppopenmp
33.1 match 46 stars 7.67 score 32 scripts 1 dependents
gpiras
sphet:Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations
Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.
Maintained by Gianfranco Piras. Last updated 6 days ago.
11.5 match 8 stars 7.43 score 188 scripts 3 dependents
kwstat
agridat:Agricultural Datasets
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
Maintained by Kevin Wright. Last updated 28 days ago.
7.0 match 125 stars 11.02 score 1.7k scripts 2 dependents
sokbae
sketching:Sketching of Data via Random Subspace Embeddings
Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80.
Maintained by Sokbae Lee. Last updated 3 years ago.
heteroskedasticityregressionsubspace-embeddingcpp
11.7 match 7 stars 4.54 score 7 scripts
jeksterslab
betaSandwich:Robust Confidence Intervals for Standardized Regression Coefficients
Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 2 months ago.
confidence-intervalsheteroskedasticity-consistent-standard-errorsstandardized-regression-coefficients
8.4 match 4.16 score 16 scripts
mbinois
hetGP:Heteroskedastic Gaussian Process Modeling and Design under Replication
Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Maintained by Mickael Binois. Last updated 6 months ago.
7.0 match 5 stars 4.89 score 260 scripts 2 dependents
pecanproject
PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in Ecological Forecasts and Reanalysis
The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.
Maintained by David LeBauer. Last updated 2 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
3.6 match 216 stars 8.91 score 15 scripts 5 dependents
saviviro
sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models
Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Maintained by Savi Virolainen. Last updated 17 days ago.
4.9 match 4 stars 6.36 score 41 scripts
cran
fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Analyze and model heteroskedastic behavior in financial time series.
Maintained by Georgi N. Boshnakov. Last updated 12 months ago.
3.6 match 6 stars 8.20 score 1.1k scripts 51 dependents
mrcieu
varGWASR:Least Absolute Deviation Regression Brown Forsythe Test
Brown-Forsythe SNP test using LAD regression and variance effect estimate
Maintained by Matthew Lyon. Last updated 2 years ago.
geneticsheteroscedasticityheteroskedasticitystatisticsvariance
10.0 match 1 stars 2.70 score
geobosh
mixAR:Mixture Autoregressive Models
Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov (2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the documentation.
Maintained by Georgi N. Boshnakov. Last updated 5 months ago.
assymetricheteroskedasticitymixture-autoregressivestudent-ttime-series
10.0 match 1 stars 2.70 score 6 scripts
stochastictree
stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference
Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.
Maintained by Drew Herren. Last updated 18 days ago.
bartbayesian-machine-learningbayesian-methodsdecision-treesgradient-boosted-treesmachine-learningprobabilistic-modelstree-ensemblescpp
3.0 match 20 stars 8.52 score 40 scripts
justinmshea
wooldridge:115 Data Sets from "Introductory Econometrics: A Modern Approach, 7e" by Jeffrey M. Wooldridge
Students learning both econometrics and R may find the introduction to both challenging. The wooldridge data package aims to lighten the task by efficiently loading any data set found in the text with a single command. Data sets have been compressed to a fraction of their original size. Documentation files contain page numbers, the original source, time of publication, and notes from the author suggesting avenues for further analysis and research. If one needs an introduction to R model syntax, a vignette contains solutions to examples from chapters of the text. Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), and are backwards compatible with all previous versions of the text.
Maintained by Justin M. Shea. Last updated 3 months ago.
2.5 match 203 stars 9.38 score 1.4k scripts
myaseen208
SupMZ:Detecting Structural Change with Heteroskedasticity
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).
Maintained by Muhammad Yaseen. Last updated 5 years ago.
5.5 match 3.70 score 1 scripts
jlopezper
whitestrap:White Test and Bootstrapped White Test for Heteroskedasticity
Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.
Maintained by Jorge Lopez Perez. Last updated 5 years ago.
5.4 match 3.32 score 40 scripts
lrberge
fixest:Fast Fixed-Effects Estimations
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Maintained by Laurent Berge. Last updated 7 months ago.
1.2 match 387 stars 14.69 score 3.8k scripts 25 dependents
jrlockwood
HETOP:MLE and Bayesian Estimation of Heteroskedastic Ordered Probit (HETOP) Model
Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>. It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.
Maintained by J.R. Lockwood. Last updated 3 years ago.
8.3 match 1 stars 2.00 score
ovgu-sh
desk:Didactic Econometrics Starter Kit
Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).
Maintained by Soenke Hoffmann. Last updated 11 months ago.
3.9 match 4.30 score 10 scripts
lbb220
GWmodel:Geographically-Weighted Models
Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Maintained by Binbin Lu. Last updated 6 months ago.
2.3 match 18 stars 6.38 score 266 scripts 4 dependents
s3alfisc
fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorslinear-regression-modelswild-bootstrapwild-cluster-bootstrapopenblascppopenmp
2.0 match 24 stars 6.67 score 109 scripts 2 dependents
r-forge
mlogit:Multinomial Logit Models
Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2009) Discrete Choice Methods with Simulations <doi:10.1017/CBO9780511805271>.
Maintained by Yves Croissant. Last updated 5 years ago.
1.3 match 9.81 score 1.2k scripts 14 dependents
bquast
rddtools:Toolbox for Regression Discontinuity Design ('RDD')
Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.
Maintained by Bastiaan Quast. Last updated 1 years ago.
1.8 match 11 stars 6.65 score 203 scripts
a91quaini
intrinsicFRP:An R Package for Factor Model Asset Pricing
Functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>, which is a modification of the prominent Hansen-Jagannathan (1997) <doi:10.1111/j.1540-6261.1997.tb04813.x> distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912> covariance estimator.
Maintained by Alberto Quaini. Last updated 8 months ago.
factor-modelsfactor-selectionfinanceidentification-testsmisspecificationrcpparmadillorisk-premiumopenblascppopenmp
2.6 match 7 stars 4.45 score 1 scripts
matthieustigler
partsm:Periodic Autoregressive Time Series Models
Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Maintained by Matthieu Stigler. Last updated 4 years ago.
2.0 match 3 stars 4.57 score 25 scripts
vdakos
earlywarnings:Early Warning Signals for Critical Transitions in Time Series
The Early-Warning-Signals Toolbox provides methods for estimating statistical changes in time series that can be used for identifying nearby critical transitions.
Maintained by Vasilis Dakos. Last updated 2 years ago.
2.3 match 3.88 score 75 scripts
neurodata
causalBatch:Causal Batch Effects
Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.
Maintained by Eric W. Bridgeford. Last updated 3 days ago.
1.7 match 4 stars 4.70 score 23 scriptsjrlockwood
eivtools:Measurement Error Modeling Tools
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression. It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
Maintained by J.R. Lockwood. Last updated 3 years ago.
3.4 match 2.26 score 18 scripts
mauricio1986
Rchoice:Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.
Maintained by Mauricio Sarrias. Last updated 2 years ago.
1.8 match 4 stars 4.03 score 42 scriptscran
AFR:Toolkit for Regression Analysis of Kazakhstan Banking Sector Data
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.
Maintained by Sultan Zhaparov. Last updated 6 months ago.
2.0 match 3.18 score
rsquaredacademy
olsrr:Tools for Building OLS Regression Models
Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Maintained by Aravind Hebbali. Last updated 4 months ago.
collinearity-diagnosticslinear-modelsregressionstepwise-regression
0.5 match 103 stars 12.19 score 1.4k scripts 4 dependents
tsmodels
tsgarch:Univariate GARCH Models
Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.
Maintained by Alexios Galanos. Last updated 3 months ago.
0.5 match 13 stars 6.93 score 16 scripts 1 dependents
bioc
transformGamPoi:Variance Stabilizing Transformation for Gamma-Poisson Models
Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.
singlecellnormalizationpreprocessingregressioncpp
0.5 match 21 stars 5.95 score 21 scripts
kolesarm
dfadjust:Degrees of Freedom Adjustment for Robust Standard Errors
Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.
Maintained by Michal Kolesár. Last updated 3 months ago.
0.5 match 31 stars 5.75 score 12 scriptscran
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.
2.1 match 1.00 score 7 scripts
mlysy
LMN:Inference for Linear Models with Nuisance Parameters
Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.
Maintained by Martin Lysy. Last updated 3 years ago.
0.5 match 1 stars 3.70 score 8 scriptscran
pretest:A Novel Approach to Predictive Accuracy Testing in Nested Environments
This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.
Maintained by Rong Peng. Last updated 1 years ago.
1.9 match 1.00 score
ctruciosm
RobGARCHBoot:Robust Bootstrap Forecast Densities for GARCH Models
Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.
Maintained by Carlos Trucios. Last updated 4 years ago.
0.5 match 3 stars 3.18 score 1 scripts
skoestlmeier
crseEventStudy:A Robust and Powerful Test of Abnormal Stock Returns in Long-Horizon Event Studies
Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <doi:10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
Maintained by Siegfried Köstlmeier. Last updated 3 years ago.
empirical-researchevent-studyfinancefinancial-analysis
0.5 match 2 stars 3.20 score 16 scripts
rainers48
tsapp:Time Series, Analysis and Application
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Maintained by Rainer Schlittgen. Last updated 3 years ago.
1.6 match 1.00 score 1 scripts
cran
YatchewTest:Yatchew (1997), De Chaisemartin & D'Haultfoeuille (2024) Linearity Test
Test of linearity originally proposed by Yatchew (1997) <doi:10.1016/S0165-1765(97)00218-8> and improved by de Chaisemartin & D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> to be robust under heteroskedasticity.
Maintained by Diego Ciccia. Last updated 8 months ago.
0.5 match 2.38 score 2 dependents
rsparapa
nftbart:Nonparametric Failure Time Bayesian Additive Regression Trees
Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at <doi:10.1111/biom.13857>.
Maintained by Rodney Sparapani. Last updated 1 years ago.
0.5 match 1.15 score 14 scriptscran
fableCount:INGARCH and GLARMA Models for Count Time Series in Fable Framework
Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood.
Maintained by Gustavo Almeida. Last updated 12 months ago.
0.5 match 1 stars 1.00 scorecran
BayesBEKK:Bayesian Estimation of Bivariate Volatility Model
The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.
Maintained by Achal Lama. Last updated 2 years ago.
0.5 match 1.00 scorecran
wbsd:Wild Bootstrap Size Diagnostics
Implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" <arXiv:2005.04089>. This diagnostic can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided.
Maintained by David Preinerstorfer. Last updated 5 years ago.
0.5 match 1.00 score