dplyr (>= 0.7.0), ggplot2 (>= 2.2.0), scales, stats

dplyr (>= 0.7.0), knitr, rmarkdown, testthat, tidyr

Functions for making run charts, Shewhart control charts and
Pareto charts for continuous quality improvement. Included control charts
are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
in the form of minor to moderate persistent shifts in data over time is
identified by the Anhoej rules for unusually long runs and unusually few
crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
Non-random variation in the form of larger, possibly transient, shifts is
identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
<doi:10.1136/qshc.2004.012047>].

c(
person(given = "Zhichang", family = "Jiang", role = c("aut")),
person(given = "Peter", family = "Song", role = c("aut"), email = "pxsong@umich.edu"),
person(given = "Michael", family = "Kleinsasser", role = c("cre"), email = "mkleinsa@umich.edu"))
Developed to perform the estimation and inference for regression
coefficient parameters in longitudinal marginal models using the method of
quadratic inference functions. Like generalized estimating equations, this
method is also a quasi-likelihood inference method. It has been showed that
the method gives consistent estimators of the regression coefficients even if
the correlation structure is misspecified, and it is more efficient than GEE
when the correlation structure is misspecified. Based on Qu, A., Lindsay,
B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Zhichang Jiang [aut],
Peter Song [aut],
Michael Kleinsasser [cre]

Open QIIME output files in R, compute statistics, and
create plots from the data.
'alpha_diversity.R' 'distance_matrix.R' 'otu_table.R'
'sample_mapping.R' 'blast.R' 'otu_mapping.R' 'qiimer.R'
'biom.R'

Qiita is a technical knowledge sharing and collaboration platform for programmers.
See <https://qiita.com/api/v2/docs> for more information.

Q-Kernel-Based and Conditionally Negative Definite Kernel-Based
Machine Learning Tools
c(person("Yusen", "Zhang", role = c("aut", "cre"), email = "yusenzhang@126.com", comment = c(ORCID = "0000-0003-3842-1153")), person("Daolin","Pang", role = c("ctb")), person("Jinghao", "Wang", role = c("ctb")), person("Jialin", "Zhang", role = c("ctb")))
Nonlinear machine learning tool for classification, clustering
and dimensionality reduction. It integrates 12 q-kernel functions and
14 conditional negative definite kernel functions and includes the
q-kernel and conditional negative definite kernel version of
density-based spatial clustering of applications with noise,
spectral clustering, generalized discriminant analysis, principal
component analysis, multidimensional scaling, locally linear embedding,
sammon's mapping and t-Distributed stochastic neighbor embedding.
Yusen Zhang [aut, cre] (<https://orcid.org/0000-0003-3842-1153>),
Daolin Pang [ctb],
Jinghao Wang [ctb],
Jialin Zhang [ctb]