• 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 152 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 69 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 (mixed-effects) models ... multivariate/multilevel models that account for non-independent sampling errors and/or true effects...
  • randtoolbox

  • Referenced in 17 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 9 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. Plug-in estimates ... give logit-normal 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]
  • Semi-Lagrangian 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 well-known 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 large-sample 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 Non-Linear and Non-Gaussian 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 non-linear 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...
  • 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 so-called sample size hyperparameter improves the robustness ... proposed method...
  • plmed

  • Referenced in 2 articles [sw40956]
  • robust methods, which do not assume partially linear mean models. Testing of the no-mediation ... effect sizes are small. We use generalized methods of moments (GMM) results to construct ... small sample performance compared with traditional tests in the partially linear setting, with drastic improvement...