determined to generate an approximation of the ground surface <https://www.mdpi.com/2072-4292/8/6/501/htm>.

Title: Rank Constrained Similarity Learning for Single Cell RNA
Authors@R: c(person("Qinglin", "Mei", email = "meiqinglinkf@163.com", role=c("aut", "cre")),
Description: A novel clustering algorithm and toolkit to accurately identify various cell types using single cell RNA sequencing data from a complex tissue. This algorithm considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. This algorithm uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similarity, and learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. See Mei et. al. (2021) <DOI:10.1101/2021.04.12.439254> for more details.
biocViews: SingleCell, Software, Clustering, DimensionReduction,
Imports: RcppAnnoy, igraph, mclust, NbClust, Rtsne, ggplot2, methods,
pracma, umap, grDevices, graphics, stats, SingleCellExperiment

Package: RCT
Title: Assign Treatments, Power Calculations, Balances, Impact
Evaluation of Experiments
Version: 1.0.0
Authors@R: person("Isidoro", "Garcia-Urquieta", email = "isidoro.gu@gmail.com",
role =c("aut", "cre"))
Description: Assists in the whole process of designing and evaluating Randomized Control Trials.
Robust treatment assignment by strata/blocks, that handles misfits;
Power calculations of the minimum detectable treatment effect
Balance tables of T-test of covariates;
Balance Regression: (treatment ~ all x variables) with F-test of null model;
Impact_evaluation: Impact evaluation regressions. This function
gives you the option to include control_vars, fixed effect variables,
cluster variables (for robust SE), multiple endogenous variables and
multiple heterogeneous variables (to test treatment effect heterogeneity)
summary_statistics: Function that creates a summary statistics table with statistics
rank observations in n groups: Creates a factor variable with n groups. Each group has
a min and max label attach to each category.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: dplyr, purrr, glue, rlang, tidyr, stringr, MASS, pracma, lfe,
broom, forcats, magrittr, ggplot2, tidyselect (>= 1.0.0)
Suggests: knitr, rmarkdown, testthat, qpdf
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-04-03 05:46:37 UTC; Isidoro
Author: Isidoro Garcia-Urquieta [aut, cre]
Maintainer: Isidoro Garcia-Urquieta <isidoro.gu@gmail.com>
Repository: CRAN
Date/Publication: 2020-04-03 17:50:05 UTC