R-universe search: exports:estfun

Showing 8 of total 8 results (show query)

r-forge

sandwich:Robust Covariance Matrix Estimators

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

Maintained by Achim Zeileis. Last updated 5 days ago.

15.06 score 11k scripts 890 dependents

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.

cpp openmp

394 stars 14.69 score 3.8k scripts 26 dependents

r-lib

generics:Common S3 Generics not Provided by Base R Methods Related to Model Fitting

In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.

Maintained by Hadley Wickham. Last updated 1 years ago.

61 stars 14.00 score 131 scripts 9.8k 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 4 months ago.

garch cpp

13 stars 6.93 score 16 scripts 1 dependents

tsmodels

tsmarch:Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Maintained by Alexios Galanos. Last updated 6 days ago.

econometrics finance garch multivariate-timeseries time-series openblas cpp

7 stars 5.80 score 3 scripts

yeagle

RWiener:Wiener Process Distribution Functions

Provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. Additionally supplies a modelling function (wdm) and further methods for the resulting object.

Maintained by Dominik Wabersich. Last updated 5 years ago.

7 stars 5.00 score 67 scripts 1 dependents

ycroissant

mhurdle:Multiple Hurdle Tobit Models

Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.

Maintained by Yves Croissant. Last updated 10 months ago.

fortran

3.88 score 15 scripts

arvsjo

ivtools:Instrumental Variables

Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.

Maintained by Arvid Sjolander. Last updated 5 years ago.

cpp

3 stars 2.85 score 39 scripts 1 dependents