Monsuru Adepeju [cre, aut],
Samuel Langton [aut],
Jon Bannister [aut]
Advances a novel adaptation of longitudinal k-means clustering
technique (Genolini et al. (2015) <doi:10.18637/jss.v065.i04>)
for grouping trajectories based on the similarities of their
long-term trends and determines the optimal solution based
on either the average silhouette width (Rousseeuw P. J. 1987)
or the Calinski-Harabatz criterion (Calinski and Harabatz (1974)
<doi:10.1080/03610927408827101>). Includes functions to extract
descriptive statistics and generate a visualisation of the
resulting groups, drawing methods from the 'ggplot2' library (Wickham H. (2016)
<doi:10.1007/978-3-319-24277-4>). The package also includes a number of
other useful functions for exploring and manipulating longitudinal
data prior to the clustering process.
kml, stats, utils, dplyr, signal, Hmisc, grDevices, ggplot2,
clusterCrit
knitr, rmarkdown, digest, gdtools, kableExtra

Estimation, regression and classification using arc lengths.

Estimation, regression and classification are redefined using a mathematically intractible measure, namely, the arc length.  Functions for parameter estimation, goodness-of-fit and regression (linear and non-linear) are provided.  These methods have been implemented with parallel computing in mind, and may therefore be executed on large clusters.

Augmented Lagrangian Adaptive Barrier Minimization
Algorithm for optimizing smooth nonlinear objective functions
with constraints. Linear or nonlinear equality and inequality
constraints are allowed.
Ravi Varadhan (with contributions from Gabor Grothendieck)

c(person("Jason", "Vander Heiden", role=c("aut", "cre"),
email="jason.vanderheiden@yale.edu"),
person("Namita", "Gupta", role=c("aut"),
email="namita.gupta@yale.edu"),
person("Susanna", "Marquez", role=c("ctb"),
email="susanna.marquez@yale.edu"),
person("Daniel", "Gadala-Maria", role=c("ctb"),
email="daniel.gadala-maria@yale.edu"),
person("Rouyi", "Jiang", role=c("ctb"),
email="ruoyi.jiang@yale.edu"),
person("Nima", "Nouri", role=c("ctb"),
email="nima.nouri@yale.edu"),
person("Steven", "Kleinstein", role=c("aut", "cph"),
email="steven.kleinstein@yale.edu"))
Immunoglobulin Clonal Lineage and Diversity Analysis
Provides immunoglobulin (Ig) sequence lineage reconstruction,
diversity profiling, and amino acid property analysis.
Citations:
Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>,
Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
dplyr (>= 0.5.0), graphics, grid, igraph (>= 1.0.0), lazyeval,
methods, progress, Rcpp (>= 0.12.12), readr, scales, seqinr,
stats, stringi, utils
'Alakazam.R' 'AminoAcids.R' 'Classes.R' 'Core.R' 'Data.R'
'Diversity.R' 'Gene.R' 'Lineage.R' 'RcppExports.R' 'Sequence.R'
'Topology.R'
Jason Vander Heiden [aut, cre],
Namita Gupta [aut],
Susanna Marquez [ctb],
Daniel Gadala-Maria [ctb],
Rouyi Jiang [ctb],
Nima Nouri [ctb],
Steven Kleinstein [aut, cph]