Showing 9 of total 9 results (show query)
cran
evd:Functions for Extreme Value Distributions
Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.
Maintained by Alec Stephenson. Last updated 6 months ago.
66.0 match 2 stars 9.46 score 748 scripts 82 dependentscran
extRemes:Extreme Value Analysis
General functions for performing extreme value analysis. In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes. Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping. Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included. For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Maintained by Eric Gilleland. Last updated 4 months ago.
26.0 match 2 stars 3.75 score 5 dependents
metrumresearchgroup
mrgsolve:Simulate from ODE-Based Models
Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.
Maintained by Kyle T Baron. Last updated 1 months ago.
8.6 match 138 stars 10.90 score 1.2k scripts 3 dependents
paulnorthrop
lax:Loglikelihood Adjustment for Extreme Value Models
Performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages 'eva' <https://cran.r-project.org/package=eva>, 'evd' <https://cran.r-project.org/package=evd>, 'evir' <https://cran.r-project.org/package=evir>, 'extRemes' <https://cran.r-project.org/package=extRemes>, 'fExtremes' <https://cran.r-project.org/package=fExtremes>, 'ismev' <https://cran.r-project.org/package=ismev>, 'mev' <https://cran.r-project.org/package=mev>, 'POT' <https://cran.r-project.org/package=POT> and 'texmex' <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the 'chandwich' package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the 'sandwich' package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.
Maintained by Paul J. Northrop. Last updated 1 years ago.
clustered-dataclusterscomposite-likelihoodevdextreme-value-analysisextreme-value-statisticsextremesindependence-loglikelihoodloglikelihood-adjustmentmlepotregressionregression-modellingrobustsandwichsandwich-estimator
15.8 match 3 stars 4.29 score 13 scripts
datarapi
Rapi:Interface for Multiple Data Providers `EDDS` and `FRED`
Interface for multiple data sources, such as the `EDDS` API <https://evds2.tcmb.gov.tr/index.php?/evds/userDocs> of the Central Bank of the Republic of Türkiye and the `FRED` API <https://fred.stlouisfed.org/docs/api/fred/> of the Federal Reserve Bank. Both data providers require API keys for access, which users can easily obtain by creating accounts on their respective websites. The package provides caching ability with the selection of periods to increase the speed and efficiency of requests. It combines datasets requested from different sources, helping users when the data has common frequencies. While combining data frames whenever possible, it also keeps all requested data available as separate data frames to increase efficiency.
Maintained by Sermet Pekin. Last updated 2 months ago.
eddsevdsevds-apievdsapifredcpp
18.0 match 1 stars 3.40 score 5 scripts
marcoolopez
tensorEVD:A Fast Algorithm to Factorize High-Dimensional Tensor Product Matrices
Here we provide tools for the computation and factorization of high-dimensional tensor products that are formed by smaller matrices. The methods are based on properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616). We evaluated this methodology by benchmark testing and illustrated its use in Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.
Maintained by Marco Lopez-Cruz. Last updated 7 months ago.
2.3 match 2 stars 4.95 score 9 scripts 1 dependents
pbiecek
ddst:Data Driven Smooth Tests
Smooth tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find two groups of smooth of test: goodness-of-fit tests and nonparametric tests for comparing distributions. Among goodness-of-fit tests there are tests for exponent, Gaussian, Gumbel and uniform distribution. Among nonparametric tests there are tests for stochastic dominance, k-sample test, test with umbrella alternatives and test for change-point problems.
Maintained by Przemyslaw Biecek. Last updated 2 years ago.
data-drivensmooth-teststatisticstest
2.0 match 6 stars 5.26 score 6 scripts 2 dependentscran
truncdist:Truncated Random Variables
A collection of tools to evaluate probability density functions, cumulative distribution functions, quantile functions and random numbers for truncated random variables. These functions are provided to also compute the expected value and variance. Nadarajah and Kotz (2006) developed most of the functions. QQ plots can be produced. All the probability functions in the stats, stats4 and evd packages are automatically available for truncation..
Maintained by Frederick Novomestky. Last updated 9 years ago.
0.5 match 3.25 score 20 dependentscran
evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation
The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.
Maintained by Carl Scarrott. Last updated 6 years ago.
0.5 match 2 stars 2.85 score 7 dependents