Showing 52 of total 52 results (show query)
yrosseel
lavaan:Latent Variable Analysis
Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Maintained by Yves Rosseel. Last updated 4 days ago.
factor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modeling
454 stars 16.82 score 8.4k scripts 218 dependents
philchalmers
mirt:Multidimensional Item Response Theory
Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.
Maintained by Phil Chalmers. Last updated 3 days ago.
212 stars 14.93 score 2.5k scripts 40 dependents
bbolker
bbmle:Tools for General Maximum Likelihood Estimation
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
Maintained by Ben Bolker. Last updated 2 months ago.
25 stars 13.36 score 1.4k scripts 117 dependents
biodiverse
unmarked:Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Maintained by Ken Kellner. Last updated 11 days ago.
4 stars 13.02 score 652 scripts 12 dependents
r-forge
copula:Multivariate Dependence with Copulas
Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.
Maintained by Martin Maechler. Last updated 25 days ago.
11.83 score 1.2k scripts 86 dependents
kingaa
pomp:Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Maintained by Aaron A. King. Last updated 10 days ago.
abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-seriesopenblas
114 stars 11.74 score 1.3k scripts 4 dependents
bioc
MAST:Model-based Analysis of Single Cell Transcriptomics
Methods and models for handling zero-inflated single cell assay data.
Maintained by Andrew McDavid. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentrnaseqtranscriptomicssinglecell
232 stars 11.28 score 1.8k scripts 5 dependents
rkillick
changepoint:Methods for Changepoint Detection
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Maintained by Rebecca Killick. Last updated 4 months ago.
133 stars 11.05 score 736 scripts 40 dependents
bcgov
ssdtools:Species Sensitivity Distributions
Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.
Maintained by Joe Thorley. Last updated 1 months ago.
ecotoxicologyenvspecies-sensitivity-distributioncpp
33 stars 10.33 score 111 scripts 5 dependentsbioc
Cardinal:A mass spectrometry imaging toolbox for statistical analysis
Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.
Maintained by Kylie Ariel Bemis. Last updated 3 months ago.
softwareinfrastructureproteomicslipidomicsmassspectrometryimagingmassspectrometryimmunooncologynormalizationclusteringclassificationregression
48 stars 10.32 score 200 scripts
stewid
SimInf:A Framework for Data-Driven Stochastic Disease Spread Simulations
Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.
Maintained by Stefan Widgren. Last updated 2 hours ago.
data-drivenepidemiologyhigh-performance-computingmarkov-chainmathematical-modellinggslopenmp
35 stars 10.11 score 227 scripts
flr
FLCore:Core Package of FLR, Fisheries Modelling in R
Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.
Maintained by Iago Mosqueira. Last updated 10 days ago.
fisheriesflrfisheries-modelling
16 stars 8.78 score 956 scripts 23 dependents
actuaryzhang
cplm:Compound Poisson Linear Models
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <doi:10.1007/s11222-012-9343-7>.
Maintained by Yanwei (Wayne) Zhang. Last updated 1 years ago.
16 stars 8.55 score 75 scripts 10 dependentsbioc
vsn:Variance stabilization and calibration for microarray data
The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.
Maintained by Wolfgang Huber. Last updated 5 months ago.
microarrayonechanneltwochannelpreprocessing
8.49 score 924 scripts 51 dependents
cran
flexmix:Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
Maintained by Bettina Gruen. Last updated 30 days ago.
5 stars 8.42 score 113 dependents
nickreich
coarseDataTools:Analysis of Coarsely Observed Data
Functions to analyze coarse data. Specifically, it contains functions to (1) fit parametric accelerated failure time models to interval-censored survival time data, and (2) estimate the case-fatality ratio in scenarios with under-reporting. This package's development was motivated by applications to infectious disease: in particular, problems with estimating the incubation period and the case fatality ratio of a given disease. Sample data files are included in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.
Maintained by Nicholas G. Reich. Last updated 2 years ago.
9 stars 8.06 score 37 scripts 8 dependents
spatpomp-org
spatPomp:Inference for Spatiotemporal Partially Observed Markov Processes
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.
Maintained by Edward Ionides. Last updated 5 months ago.
2 stars 7.38 score 93 scriptscran
sn:The Skew-Normal and Related Distributions Such as the Skew-t and the SUN
Build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t and the SUN families. For the skew-normal and the skew-t distributions, statistical methods are provided for data fitting and model diagnostics, in the univariate and the multivariate case.
Maintained by Adelchi Azzalini. Last updated 2 years ago.
3 stars 7.37 score 91 dependentskingaa
ouch:Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Maintained by Aaron A. King. Last updated 5 months ago.
adaptive-regimebrownian-motionornstein-uhlenbeckornstein-uhlenbeck-modelsouchphylogenetic-comparative-hypothesesphylogenetic-comparative-methodsphylogenetic-datareact
15 stars 6.87 score 68 scripts 4 dependents
ingmarvisser
depmixS4:Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Maintained by Ingmar Visser. Last updated 4 years ago.
12 stars 6.85 score 308 scripts 4 dependentscran
topicmodels:Topic Models
Provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.
Maintained by Bettina Grün. Last updated 8 months ago.
8 stars 6.37 score 16 dependents
jeswheel
panelPomp:Inference for Panel Partially Observed Markov Processes
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Maintained by Jesse Wheeler. Last updated 4 months ago.
5.91 score 45 scripts
quantsulting
ghyp:Generalized Hyperbolic Distribution and Its Special Cases
Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005).
Maintained by Marc Weibel. Last updated 7 months ago.
5.55 score 90 scripts 8 dependents
ajmcneil
tscopula:Time Series Copula Models
Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.
Maintained by Alexander McNeil. Last updated 1 months ago.
2 stars 5.53 score 12 scriptsbioc
tilingArray:Transcript mapping with high-density oligonucleotide tiling arrays
The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations.
Maintained by Zhenyu Xu. Last updated 5 months ago.
microarrayonechannelpreprocessingvisualization
5.48 score 5 scripts 1 dependents
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scriptsr-forge
R2MLwiN:Running 'MLwiN' from Within R
An R command interface to the 'MLwiN' multilevel modelling software package.
Maintained by Zhengzheng Zhang. Last updated 11 days ago.
5.35 score 125 scripts
beanumber
tidychangepoint:A Tidy Framework for Changepoint Detection Analysis
Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.
Maintained by Benjamin S. Baumer. Last updated 2 months ago.
2 stars 5.30 score 8 scriptscran
aod:Analysis of Overdispersed Data
Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).
Maintained by Renaud Lancelot. Last updated 1 years ago.
3 stars 5.15 score 15 dependents
poissonconsulting
bboutools:Boreal Caribou Survival, Recruitment and Population Growth
Estimates annual survival, recruitment and population growth for boreal caribou populations using Bayesian and Maximum Likelihood models with fixed and random effects.
Maintained by Seb Dalgarno. Last updated 2 months ago.
1 stars 5.11 score 13 scripts 2 dependents
parksw3
fitode:Tools for Ordinary Differential Equations Model Fitting
Methods and functions for fitting ordinary differential equations (ODE) model in 'R'. Sensitivity equations are used to compute the gradients of ODE trajectories with respect to underlying parameters, which in turn allows for more stable fitting. Other fitting methods, such as MCMC (Markov chain Monte Carlo), are also available.
Maintained by Sang Woo Park. Last updated 1 months ago.
6 stars 5.01 score 34 scriptsbioc
fmrs:Variable Selection in Finite Mixture of AFT Regression and FMR Models
The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net.
Maintained by Farhad Shokoohi. Last updated 5 months ago.
survivalregressiondimensionreduction
3 stars 5.00 score 55 scripts 1 dependents
yxlin
ggdmc:Cognitive Models
The package provides tools to fit the LBA, DDM, PM and 2-D diffusion models, using the population-based Markov Chain Monte Carlo.
Maintained by Yi-Shin Lin. Last updated 8 months ago.
19 stars 4.66 score 24 scripts
juhkim111
MGLM:Multivariate Response Generalized Linear Models
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Maintained by Juhyun Kim. Last updated 3 years ago.
4 stars 4.65 score 53 scripts 1 dependentspoissonconsulting
embr:Model Builder Utility Functions and Virtual Classes
Utility functions and virtual classes shared by model builder packages such as tmbr, jmbr and smbr.
Maintained by Joe Thorley. Last updated 2 months ago.
3 stars 4.61 score 4 scripts 3 dependentsbioc
INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data
INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.
Maintained by Stefano de Pretis. Last updated 5 months ago.
sequencingrnaseqgeneregulationtimecoursesystemsbiology
4.38 score 9 scripts
pi-kappa-devel
markets:Estimation Methods for Markets in Equilibrium and Disequilibrium
Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) <doi:10.18637/jss.v108.i02> for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics.
Maintained by Pantelis Karapanagiotis. Last updated 1 years ago.
disequilibriumeconomicsfinancefull-information-maximum-likelihoodmarket-clearingmarket-modelsshort-side-rulecpp
1 stars 4.30 score 9 scripts
hdakpo
sfaR:Stochastic Frontier Analysis Routines
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021) <doi:10.1111/agec.12683>. Several possibilities in terms of optimization algorithms are proposed.
Maintained by K Hervé Dakpo. Last updated 5 months ago.
4 stars 4.08 score 9 scripts 1 dependents
tvatter
gamCopula:Generalized Additive Models for Bivariate Conditional Dependence Structures and Vine Copulas
Implementation of various inference and simulation tools to apply generalized additive models to bivariate dependence structures and non-simplified vine copulas.
Maintained by Thibault Vatter. Last updated 5 years ago.
9 stars 3.77 score 13 scripts
kevinmcgregor
countprop:Calculate Model-Based Metrics of Proportionality on Count-Based Compositional Data
Calculates metrics of proportionality using the logit-normal multinomial model. It can also provide empirical and plugin estimates of these metrics.
Maintained by Kevin McGregor. Last updated 2 years ago.
3.70 score 3 scripts
ksublee
emhawkes:Exponential Multivariate Hawkes Model
Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
Maintained by Kyungsub Lee. Last updated 2 months ago.
5 stars 3.40 score 10 scripts
niamhmimnagh
MultiNMix:Multi-Species N-Mixture (MNM) Models with 'nimble'
Simulating data and fitting multi-species N-mixture models using 'nimble'. Includes features for handling zero-inflation and temporal correlation, Bayesian inference, model diagnostics, parameter estimation, and predictive checks. Designed for ecological studies with zero-altered or time-series data. Mimnagh, N., Parnell, A., Prado, E., & Moral, R. A. (2022) <doi:10.1007/s10651-022-00542-7>. Royle, J. A. (2004) <doi:10.1111/j.0006-341X.2004.00142.x>.
Maintained by Niamh Mimnagh. Last updated 27 days ago.
1 stars 3.18 score
gilberto-sassi
geoFourierFDA:Ordinary Functional Kriging Using Fourier Smoothing and Gaussian Quadrature
Implementation of the ordinary functional kriging method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>. This implements an alternative method to estimate the trace-variogram using Fourier Smoothing and Gaussian Quadrature.
Maintained by Gilberto Sassi. Last updated 4 years ago.
2.70 score 3 scriptsflr
bbm:FLR Implementation of a Two-Stage Stock Assessment Model
The two-stage biomass-based model for the Bay of Biscay anchovy (Ibaibarriaga et al., 2008).
Maintained by Leire Ibaibarriaga. Last updated 3 years ago.
2.70 score 5 scriptscran
twopartm:Two-Part Model with Marginal Effects
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Maintained by Yajie Duan. Last updated 2 years ago.
3 stars 2.18 score
skoval
blm:Binomial Linear Regression
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maintained by S.Kovalchik. Last updated 3 years ago.
1.41 score 26 scripts
bpfaff
gogarch:Generalized Orthogonal GARCH (GO-GARCH) Models
Provision of classes and methods for estimating generalized orthogonal GARCH models. This is an alternative approach to CC-GARCH models in the context of multivariate volatility modeling.
Maintained by Bernhard Pfaff. Last updated 3 years ago.
1.26 score 18 scripts
apedrods
MAINT.Data:Model and Analyse Interval Data
Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Maintained by Pedro Duarte Silva. Last updated 2 years ago.
1.15 score 14 scripts