Decomposition of Bulk Expression with Single-Cell Sequencing
c(
person(given = 'Brandon', family = 'Jew', email = 'brandon.jew@ucla.edu', role = c('aut', 'cre')),
person(given = 'Marcus', family = 'Alvarez', role = 'aut')
)
Provides tools to accurately estimate cell type abundances
from heterogeneous bulk expression. A reference-based method utilizes
single-cell information to generate a signature matrix and transformation
of bulk expression for accurate regression based estimates. A marker-based
method utilizes known cell-specific marker genes to measure relative
abundances across samples.
For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Seurat, plyr, knitr, rmarkdown, testthat
Brandon Jew [aut, cre],
Marcus Alvarez [aut]

Ricardo Puziol de Oliveira and Jorge Alberto Achcar
Ricardo Puziol de Oliveira <rpuziol.oliveira@gmail.com>
Computes the joint probability mass function, the joint cumulative distribution, the correlation coefficient and generate random deviates from the Basu-Dhar bivariate geometric distribution. The package also computes the joint survival function in presence of cure fraction using the mixture model approach (For more details, see Achcar et. al (2016) <doi:10.1080/02664763.2015.1117589> and de Oliveira et. al (2018) <doi:10.1080/02664763.2018.1495701>).

Computes the joint probability mass function (pmf), the joint cumulative function (cdf), the joint survival function (sf), the correlation coefficient, the covariance, the cross-factorial moment and generate random deviates for the Basu-Dhar bivariate geometric distribution as well the joint probability mass, cumulative and survival function assuming the presence of a cure fraction given by the standard bivariate mixture cure fraction model. The package also computes the estimators based on the method of moments.
For more details, see de Oliveira et. al (2018)
<doi:10.1285/i20705948v11n1p108>.

Bivariate Alternating Recurrent Event Data Analysis
c(person("Sandra", "Castro-Pearson", email= "cast0135@umn.edu", role = c("aut", "cre")), person("Chi-Hyun", "Lee", email = "chihyunlee@umass.edu", role ="aut"), person("Chiung-Yu", "Huang", email = "chiungyu.huang@ucsf.edu", role = "aut"),
person("Xianghua", "Luo", email = "luox0054@umn.edu", role = "ctb"))
Sandra Castro-Pearson [aut, cre],
Chi-Hyun Lee [aut],
Chiung-Yu Huang [aut],
Xianghua Luo [ctb]
Alternating recurrent event data arise frequently in biomedical and social sciences where 2 types of events such as hospital admissions and discharge occur alternatively over time.
As such we implement a collection of non-parametric and semiparametric methods to analyze such data.
The main functions are biv.rec.fit() and biv.rec.np(). Use biv.rec.fit() for estimation of covariate effects on the two alternating event gap times (xij and yij) using semiparametric methods. The method options are "Lee.et.al" and "Chang".
Use biv.rec.np() for estimation of the joint cumulative distribution function (cdf) for the two alternating events gap times (xij and yij) as well as the marginal survival function for type I gap times (xij) and the conditional cdf of the type II gap times (yij) given an interval of type I gap times (xij) in a non-parametric fashion.
The package also provides options to simulate and visualize the data and results of analysis.
MASS, stringr, utils, knitr, rmarkdown, Rcpp, graphics,
grDevices