Man pages for feng-li/movingknots
Efficient Bayesian multivariate surface regression
| aT.x.DvecSigma4beta.inv | <description> |
| convert.densParams | Convert the parameters corresponding to different... |
| delta.K | Give the gradient for a diagnoal matrix (K) w.r.t. its... |
| delta.sumlogdetP | <description> |
| delta.sumqlogdetK | gradient for sum q_i log|K_i| |
| delta.vecPartiSigma4beta.inv | This is the gradient for a single partition of beta's... |
| delta.xi | Gradient with respect to the knots locations. |
| deriv_prior | A collection of gradient for common priors. |
| DGP.hwang | A collection of DGPs for different models from Hwang's... |
| DGP.surface | DGP Surface nested |
| FitDiagnosis | Diagnosis if the spline model |
| FitDiagnosis.hwang | Diagnosis if the spline model |
| glm_gradhess | GLM |
| glm_logpost | The conditional and joint log posterior function |
| gradient_vecB | Gradient w.r.t. vecB |
| gradient_xi | gradient w.r.t. xi |
| gradient_xi_condi | Gradient w.r.t xi (conditional method) |
| grad_vech_Sigma | The gradient with respect to vech Sigma |
| grad.x.deriv_link | Gradient derivative for x wrt link function |
| idx.b2beta | Make indices from b to beta. |
| idx.beta2b | Convert indices from beta to b |
| knots_check_boundary | Boundary check for the moving knots model It is easy to check... |
| knots_list2mat | Convert list of knots into a n-by-1 matrix. |
| knots_mat2list | Convert the knots matrix into the knots list |
| knots_subsetsIdx | Find the locations of subsets in the knots matrix |
| KStepNewtonMove | Newton move for spline models without dimession changes. |
| linear_gradhess | Gradient and Hessian matrix for the "marginal posterior" for... |
| linear_IWishart | Calculate the posterior df and location matrix V from the... |
| linear_logpost | The conditional and joint log posterior function |
| linear_post4coef | Direct sample the coefficients from normal distribution. |
| LogPredScore | Log predictive scores |
| log_prior | logarithm density for priors |
| make.knotsPriVar | Set the prior variance of the knots. |
| Mat.delta.xi | <description> |
| Mat.x.AT.k.I.x.K.x.delta.knots | <description> |
| Mat.x.delta.vecSigma4beta.inv | Compute a matrix to pre-multiply the gradient for vec beta's... |
| Mat.x.DvecA.k.P_stp1 | Perform a dense matrix multiplying by Dev[vec[A K_qi,qi) |
| Mat.x.DvecA.k.P_stp2 | <description> |
| Mat.x.DvecA.k.XTX_stp1 | Perform a dense matrix multiplying by Dev[vec[A K_qi,qi) |
| Mat.x.DvecA.k.XTX_stp2 | <description> |
| Mat.x.DvecSigma.inv.k.XTX | <description> |
| MCMC.trajectory | Trajectory MCMC for movingknots |
| MHPropMain | The Main MCMC algorithm for movingknots. |
| MHPropWithIWishart | Random walk Metropolis–Hastings algorithm for Sigma |
| MHPropWithKStepNewton | Metropolis–Hastings algorithm with K-step Newton method for... |
| MovingKnots_MCMC | MCMC for movingknots. |
| Params.subsets | Organize the subsets of the parameters by taking away the... |
| par.transform | Parameter transformation. |
| par.transform2 | Transform the parameters from the original scale to the new... |
| P.matrix | Generate the P matrices for the moving knots model |
| PredSurface | Surface prediction for the movingknots. |
| RandomWalkMetropolis | Random walk Metropolis algorithm for movingknots. |
| SGLD | Stochastic MCMC using Stochastic gradient Langevin dynamics |
| Sigma4betaFun | Calculate the variance for the prior of coefficients (beta)... |
| sub.hessian | Extract the subset of hessian matrix. |
| surface.hwang | Surface from Hwang paper. |
| Xmats.x.delta.xi | Preform X_i multiply Dev xi |
| X.x.delta.xi | Preform X multiply Dev xi |
