Showing 18 of total 18 results (show query)
tnagler
VineCopula:Statistical Inference of Vine Copulas
Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.
Maintained by Thomas Nagler. Last updated 9 days ago.
copulaestimationstatisticsvine
92 stars 11.07 score 362 scripts 23 dependents
ikosmidis
brglm2:Bias Reduction in Generalized Linear Models
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).
Maintained by Ioannis Kosmidis. Last updated 7 months ago.
adjusted-score-equationsalgorithmsbias-reducing-adjustmentsbias-reductionestimationglmlogistic-regressionnominal-responsesordinal-responsesregressionregression-algorithmsstatistics
32 stars 10.41 score 106 scripts 10 dependents
acclab
dabestr:Data Analysis using Bootstrap-Coupled Estimation
Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.
Maintained by Yishan Mai. Last updated 1 years ago.
data-analysisdata-visualizationestimationstatistics
214 stars 9.80 score 142 scripts
psolymos
ResourceSelection:Resource Selection (Probability) Functions for Use-Availability Data
Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>, Lele (2009) <doi:10.2193/2007-535>, and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
Maintained by Peter Solymos. Last updated 11 months ago.
ecologyestimationlelersfrspfsolymosweighted-distributions
8 stars 8.37 score 752 scripts 3 dependents
jonasmoss
univariateML:Maximum Likelihood Estimation for Univariate Densities
User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.
Maintained by Jonas Moss. Last updated 30 days ago.
densityestimationmaximum-likelihood
8 stars 8.19 score 62 scripts 7 dependents
bsaul
geex:An API for M-Estimation
Provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.
Maintained by Bradley Saul. Last updated 11 months ago.
asymptoticscovariance-estimatescovariance-estimationestimate-parametersestimating-equationsestimationinferencem-estimationrobustsandwich
8 stars 7.70 score 131 scripts 2 dependents
vinecopulib
rvinecopulib:High Performance Algorithms for Vine Copula Modeling
Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.7.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
Maintained by Thomas Nagler. Last updated 9 days ago.
copulaestimationstatisticsvinecpp
35 stars 7.43 score 60 scripts 14 dependents
kkholst
targeted:Targeted Inference
Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
Maintained by Klaus K. Holst. Last updated 2 months ago.
causal-inferencedouble-robustestimationsemiparametric-estimationstatisticsopenblascppopenmp
11 stars 7.20 score 30 scripts 1 dependents
leifeld
btergm:Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
Maintained by Philip Leifeld. Last updated 15 days ago.
complex-networksdynamic-analysisergmestimationgoodness-of-fitinferencelongitudinal-datanetwork-analysispredictiontergm
18 stars 7.03 score 83 scripts 2 dependentstnagler
vinereg:D-Vine Quantile Regression
Implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.
Maintained by Thomas Nagler. Last updated 3 months ago.
copulaestimationstatisticsvinecpp
11 stars 5.76 score 26 scripts
blasif
cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.
Maintained by Federico Blasi. Last updated 2 months ago.
covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationpredictioncpp
3 stars 5.48 score 1 scripts
gabrielerovigatti
prodest:Production Function Estimation
TFP estimation with the control function approach.
Maintained by Gabriele Rovigatti. Last updated 5 years ago.
estimationproductivitystatatfp
37 stars 4.87 score 20 scripts
jan-imbi
adestr:Estimation in Optimal Adaptive Two-Stage Designs
Methods to evaluate the performance characteristics of various point and interval estimators for optimal adaptive two-stage designs as described in Meis et al. (2024) <doi:10.1002/sim.10020>. Specifically, this package is written to work with trial designs created by the 'adoptr' package (Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09>; Pilz et al. (2021) <doi:10.1002/sim.8953>)). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values.
Maintained by Jan Meis. Last updated 9 months ago.
adaptiveadoptrconfidencedesignsestimationintervalsoptimalparameterpointtwo-stage
4.08 score 12 scripts
ogarciav
resde:Estimation in Reducible Stochastic Differential Equations
Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.
Maintained by Oscar Garcia. Last updated 2 years ago.
estimationstochastic-differential-equations
2 stars 4.00 score 2 scripts
dazzimonti
anMC:Compute High Dimensional Orthant Probabilities
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Maintained by Dario Azzimonti. Last updated 2 years ago.
estimationgaussianorthantprobabilityopenblascpp
3.88 score 6 scripts 5 dependents
geobosh
StableEstim:Estimate the Four Parameters of Stable Laws using Different Methods
Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.
Maintained by Georgi N. Boshnakov. Last updated 5 months ago.
characteristic-functionsestimationsimulationstable-distribution
3.73 score 18 scripts 2 dependents
glotaran
paramGUI:A Shiny GUI for some Parameter Estimation Examples
Allows specification and fitting of some parameter estimation examples inspired by time-resolved spectroscopy via a Shiny GUI.
Maintained by Joris Snellenburg. Last updated 2 years ago.
educationestimationparameter-estimationshinyteaching
2 stars 3.00 score 6 scripts
daandejongen
hystar:Fit the Hysteretic Threshold Autoregressive Model
Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.
Maintained by Daan de Jong. Last updated 1 years ago.
autoregressionestimationhysteresissimulationstatisticsthresholdtime-series-analysiscpp
2.70 score 3 scripts