
bootlib
 Referenced in 426 articles
[sw40642]
 extends the basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals ... chapters deal with resampling methods appropriate for linear regression models, generalized linear models and nonlinear...

DGM
 Referenced in 182 articles
[sw39282]
 network is trained on batches of randomly sampled time and space points. The algorithm ... equation. The deep learning algorithm approximates the general solution to the Burgers’ equation ... Deep Galerkin method (DGM)” since it is similar in spirit to Galerkin methods, with ... neural network instead of a linear combination of basis functions. In addition, we prove...

DPpackage
 Referenced in 72 articles
[sw10495]
 spaces are highly complex and hence sampling methods play a key role. This paper provides ... clustered data using generalized linear mixed models, and regression data using generalized additive models ... Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency...

metafor
 Referenced in 33 articles
[sw12291]
 method, Peto’s method, and a variety of suitable generalized linear (mixedeffects) models ... multivariate/multilevel models that account for nonindependent sampling errors and/or true effects...

randtoolbox
 Referenced in 19 articles
[sw07969]
 general linear congruential generators (Park Miller) and multiple recursive generators (Knuth TAOCP), generalized feedback shift ... random’ package, for Latin Hypercube Sampling (a hybrid QMC method), use the ’lhs’ package...

bernor
 Referenced in 10 articles
[sw19665]
 available in closed form. This method uses simulated missing data that are independent and identically ... both Monte Carlo and observed data sample sizes go to infinity simultaneously. Plugin estimates ... give logitnormal generalized linear mixed model examples, calculated using an R package...

mcprofile
 Referenced in 1 article
[sw16827]
 small sample inference for linear combinations of generalized linear model parameters. A method is proposed...

PSOPT
 Referenced in 8 articles
[sw20700]
 Kutta integrators. With both global and local methods, differential equations, continuous constraints and integrals associated ... General event constraints; Integral constraints; Interior point constraints; Bounds on controls and state variables; General ... cost function with Lagrange and Mayer terms. Linear or nonlinear linkages between phases; Fixed ... static parameters; Optimal parameter estimation given sampled observations...

StOpt
 Referenced in 10 articles
[sw32903]
 SemiLagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled ... Equations (C++ only). Stochastic Dual Dynamic Programming methods to deal with stochastic stocks management problems ... mapped to python (examples provided). Some methods are provided to solve by Monte Carlo some ... Carlo Methods are proposed to solve some non linear PDEs. For each method, a framework...

robNB
 Referenced in 2 articles
[sw27012]
 provides functions for fitting negative binomial generalized linear models to count data both by maximum ... likelihood and by robust (bounded influence) methods. It also provides many different tests, including saddlepoint ... known for their high accuracy in small samples. For an overview and more details...

LSDE
 Referenced in 7 articles
[sw15811]
 process. In the linear case, the nonlinear parameter restrictions of the sampled model ... time measurement updates. Included are the classical methods of extended Kalman filtering and higher order ... Hermite expansion of probability densities to obtain generalized Gauss filters (GGHF) utilizing higher order moments...

PLASQ
 Referenced in 6 articles
[sw18746]
 approach is based upon a generalized linear model that takes advantage of a novel classification ... variety of cell types. In normal diploid samples, PLASQ is able to genotype with very ... able to provide a generalized genotype in cancer samples (e.g., CCCCT at an amplified ... means. An R software package containing the methods is freely available...

glmmsr
 Referenced in 2 articles
[sw40199]
 inference about generalized linear mixed models, with a choice about which method ... sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation...

UODV
 Referenced in 4 articles
[sw02422]
 Optimal Discrimination Vectors (UODV) is an effective linear discrimination approach. However, this approach ... training samples. Then, a new and generalized theorem on UODV is presented. This generalized theorem ... between UODV and the wellknown Fisherface method, and proves that our improved UODV algorithm...

boottest
 Referenced in 5 articles
[sw37419]
 methods in general, the wild bootstrap is especially useful when conventional inference methods are unreliable ... because largesample assumptions do not hold. For example, there may be few clusters ... postestimation command, boottest works after linear estimation commands, including regress, cnsreg, ivregress, ivreg2, areg...

bssm
 Referenced in 1 article
[sw28273]
 Inference of NonLinear and NonGaussian State Space Models. Efficient methods for Bayesian inference ... Markov chain Monte Carlo and parallel importance sampling type weighted Markov chain Monte Carlo (Vihola ... Gaussian state dynamics, as well as general nonlinear Gaussian models and discretised diffusion models...

ReGenesees
 Referenced in 1 article
[sw18868]
 Shares, Multiple Regression Coefficients and Quantiles. Automated Linearization of Complex Analytic Estimators. Design Covariance ... estimators. Estimates and sampling errors for subpopulations. Generalized Variance Functions (GVF) method for predicting variance...

RENT
 Referenced in 1 article
[sw39621]
 Elastic Net Technique) is a feature selection method for binary classification and regression problems ... core RENT trains an ensemble of generalized linear models using regularized elastic net to select ... ensemble is trained using a randomly, iid sampled subset of rows of the full training...

MultiwayRegression
 Referenced in 3 articles
[sw18394]
 regression. We propose a framework for the linear prediction of a multiway array ... tensor product. This framework generalizes several existing approaches, including methods to predict a scalar outcome ... have reduced CPrank. We propose a general and efficient algorithm for penalized leastsquares ... Bayesian posterior, which motivates a Gibbs sampling algorithm for inference. We illustrate the approach with...

ManiSolve
 Referenced in 1 article
[sw34623]
 decomposition, for the proposed method the computational effort grows only linearly with the problem size ... found in the general case, increasing the socalled sample size hyperparameter improves the robustness ... proposed method...