graph, RBGL, precrec, preprocessCore, methods, plyr, foreach,
doParallel

graph, RBGL, precrec, preprocessCore, methods, plyr, foreach,
doParallel, parallel

MLE and Bayesian Estimation of Heteroskedastic Ordered Probit
(HETOP) Model
Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>.  It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.

excel connections, garbage collection,string matching and passing by reference
package containing functions for excel connections and string matching
and passing by reference

csv import, csv export, garbage collection,string matching and passing by reference
package containing functions for convenient csv import and export with a view of using R as a calculation back end to spreadsheet-like programs such as scalc in open office; string matching and passing by reference

csv import, csv export, garbage collection,string matching and
passing by reference
package containing functions for convenient csv import and
export with a view of using R as a calculation back end to
spreadsheet-like programs such as scalc in open office; string
matching and passing by reference

Orphaned on 2012-09-18 as requested by the original
maintainer, Felix Wittmann <hfwittmann at gmail.com>

Heterogeneous Graphical Model for Non-Negative Data
Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.
c(person("Jiaqi" , "Zhang", role = c("aut", "cre"), email = "boarzhang@gmail.com"),
person("Xinyan", "Fan"  , role = c("aut")),
person("Yang"  , "Li"   , role = c("aut")))
Jiaqi Zhang [aut, cre],
Xinyan Fan [aut],
Yang Li [aut]