• # AIS-BN

• Referenced in 25 articles [sw02223]
• importance function, and (3) a dynamic weighting function for combining samples from different stages ... general purpose sampling algorithms, likelihood weighting and self-importance sampling. We used in our tests...
• # AWS

• Referenced in 43 articles [sw04034]
• package aws: Adaptive Weights Smoothing. The package contains R-functions implementing the Propagation-Separation Approach ... Spokoiny (2006), Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields...
• # cmm

• Referenced in 5 articles [sw24365]
• Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal...
• # ForestFit

• Referenced in 1 article [sw31035]
• moment, percentile, rank correlation, least square, weighted maximum likelihood, U-statistic, weighted least square ... three-parameter case are: maximum likelihood, modified moment type 1, modified moment type 2, modified ... product spacing, T-L moment, and weighted maximum likelihood. III) The Bayesian estimators...
• # TagProp

• Referenced in 13 articles [sw11682]
• model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance ... metric learning by directly maximizing the log-likelihood of the tag predictions in the training ... word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall...
• # MAMSE

• Referenced in 0 articles [sw18460]
• weights can be used in a weighted likelihood or to define a mixture of empirical ... package includes functions for the MAMSE weighted Kaplan-Meier estimate and for MAMSE weighted...
• # RAxML-Light

• Referenced in 2 articles [sw29595]
• supercomputers under maximum likelihood. It implements a light-weight checkpointing mechanism, deploys 128-bit (SSE3 ... level message passing interface parallelization of the likelihood function. To demonstrate scalability and robustness...
• # N-way Toolbox

• Referenced in 28 articles [sw12996]
• using expectation maximization); Fitting models with a weighted least squares loss function (including MILES); Predicting ... generalized multiplicative ANOVA, MILES for maximum likelihood fitting, conload for congruence and correlation loadings, eemscat...
• # selectMeta

• Referenced in 1 article [sw06784]
• discuss how to estimate a decreasing weight function in the above model and illustrate ... examples. Some basic properties of the log-likelihood function and computation of a $p$-value ... weight function are indicated. In addition, we provide an approximate selection bias adjusted profile likelihood...