A Markov chain Monte Carlo method is provided to estimate the preferential attachment function from a single network snapshot. Conventional methods require the complete information about the appearance order of all nodes and edges in the network. This package incorporates the appearance order into the state space and estimates it together with the preferential attachment function.

Visualize Results of Statistical Hypothesis Tests
c(person("Michael", "Czekanski", email = "mczekanski@middlebury.edu", role = c("aut", "cre")), person("Alex", "Lyford", email = "alyford@middlebury.edu", role = "aut"))
Provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College.
dplyr, ggplot2, ggthemes, gridExtra, magrittr, tidyr
Michael Czekanski [aut, cre],
Alex Lyford [aut]

Provides functionality to produce graphs of sampling distributions of test 	statistics from a variety of common statistical tests. With only a few keystrokes, 	the user can conduct a hypothesis test and visualize the test statistic and 		corresponding p-value through the shading of its sampling distribution. Initially       	created for statistics at Middlebury College.
dplyr, ggplot2, ggthemes, gridExtra, magrittr, rlang, stats,
tidyr

When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for 'mcb'.