networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling [0.03%]
网络仿真包Networksis：通过顺序重要性采样法模拟固定边界的二分图
Ryan Admiraal,Mark S Handcock
Ryan Admiraal
The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals...
simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data [0.03%]
具有复杂纵向数据的因果效应估计的模拟研究的透明化与再现性——兼论simcausal R软件包的功能
Oleg Sofrygin,Mark J van der Laan,Romain Neugebauer
Oleg Sofrygin
The simcausal R package is a tool for specification and simulation of complex longitudinal data structures that are based on non-parametric structural equation models. The package aims to provide a flexible tool for simplifying the conduct ...
Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package [0.03%]
使用pcnetmeta包在R中进行基于臂的网络 meta 分析
Lifeng Lin,Jing Zhang,James S Hodges et al.
Lifeng Lin et al.
Network meta-analysis is a powerful approach for synthesizing direct and indirect evidence about multiple treatment comparisons from a collection of independent studies. At present, the most widely used method in network meta-analysis is co...
Fitting Position Latent Cluster Models for Social Networks with latentnet [0.03%]
具有位置潜在聚类结构的社交网络分析包latentnet
Pavel N Krivitsky,Mark S Handcock
Pavel N Krivitsky
latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of char...
A SAS Macro for Covariate-Constrained Randomization of General Cluster-Randomized and Unstratified Designs [0.03%]
一种用于一般集群随机化和未分层设计的协变量约束随机化的SAS宏命令
Erich J Greene
Erich J Greene
Ivers et al. (2012) have recently stressed the importance to both statistical power and face validity of balancing allocations to study arms on relevant covariates. While several techniques exist (e.g., minimization, pair-matching, stratifi...
Hana Ševčíková,Adrian E Raftery
Hana Ševčíková
We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sam...
bayesTFR: An R Package for Probabilistic Projections of the Total Fertility Rate [0.03%]
bayesTFR包：总和生育率的概率预测的R语言软件包
Hana Ševčíková,Leontine Alkema,Adrian E Raftery
Hana Ševčíková
The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rate (TFR) for all countries. In the model, a random walk with drift is used to project the TFR during the fertility transiti...
John A Kairalla,Christopher S Coffey,Keith E Muller
John A Kairalla
Internal pilot designs involve conducting interim power analysis (without interim data analysis) to modify the final sample size. Recently developed techniques have been described to avoid the type I error rate inflation inherent to unadjus...
JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data [0.03%]
JMFit：一个用于纵向数据和生存数据分析联合模型的SAS宏程序
Danjie Zhang,Ming-Hui Chen,Joseph G Ibrahim et al.
Danjie Zhang et al.
Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for a longitudinal biomarker such as patient-report...
PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes [0.03%]
基于狄利克雷过程的轮廓回归混合模型的R语言PReMiuM程序包
Silvia Liverani,David I Hastie,Lamiae Azizi et al.
Silvia Liverani et al.
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster mem...