Showing 10 of total 10 results (show query)
gggchen
HAC:Estimation, Simulation and Visualization of Hierarchical Archimedean Copulae (HAC)
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Maintained by Gong Chen. Last updated 6 months ago.
86.7 match 4.18 score 52 scripts 1 dependents
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 2 months ago.
8.8 match 14.92 score 11k scripts 887 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.
3.8 match 387 stars 14.69 score 3.8k scripts 25 dependents
hvillalo
echogram:Echogram Visualisation and Analysis
Easily import multi-frequency acoustic data stored in 'HAC' or 'RAW' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
Maintained by Héctor Villalobos. Last updated 1 years ago.
12.6 match 3 stars 3.45 score 19 scripts
keblu
nse:Numerical Standard Errors Computation in R
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Maintained by Keven Bluteau. Last updated 2 years ago.
11.0 match 3.56 score 12 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 9 days ago.
2.2 match 8 stars 7.43 score 188 scripts 3 dependents
jphill01
HACSim:Iterative Extrapolation of Species' Haplotype Accumulation Curves for Genetic Diversity Assessment
Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.
Maintained by Jarrett D. Phillips. Last updated 6 months ago.
dna-barcodinghaplotype-accumulation-curvescpp
4.5 match 3.48 score 5 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.
8.5 match 1.00 score 1 scripts
zerokgkg1368
tensorMiss:Handle Missing Tensor Data with C++ Integration
To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base 'array' class to some data classes, this package serves as an alternative resort to handle tensor only as 'array' class. Some functionalities related to missingness are also supported.
Maintained by Zetai Cen. Last updated 11 months ago.
1.7 match 3.08 score 2 dependentszerokgkg1368
MEFM:Perform MEFM Estimation on Matrix Time Series
To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.
Maintained by Zetai Cen. Last updated 10 months ago.
1.7 match 2.48 score 7 scripts 1 dependents