Design and Analyze Studies using the Sequential Parallel
Comparison Design
Programs to find the sample size or power of studies using the
Sequential Parallel Comparison Design (SPCD) and programs to analyze
such studies. This is a clinical trial design where
patients initially on placebo who did not respond are re-randomized between
placebo and active drug in a second phase and the results of the two phases are
pooled. The method of analyzing binary data with this design is described in
Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the
method of analyzing continuous data is described in Chen, Yang,
Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.

Compute Power or Sample Size for GWAS with Covariate Effect
Fast computation of the required sample size or the
achieved power, for GWAS studies with different types of covariate
effects and different types of covariate-gene dependency structure.
For the detailed description of the methodology, see Zhang (2022)
"Power and Sample Size Computation for Genetic Association Studies
of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.

Statistical package for species richness estimation
SPECIES is an R package for estimation of species richness
or diversity.

Statistical Package for Species Richness Estimation
Implementation of various methods in estimation of species richness or diversity in Wang (2011)<doi:10.18637/jss.v040.i09>.

Receptor Abundance Estimation using Reduced Rank Reconstruction
and Clustered Thresholding
c(
person("H. Robert", "Frost", email = "hildreth.r.frost@dartmouth.edu", role = c("aut")),
person("Azka", "Javaid", email = "azka.javaid.gr@dartmouth.edu", role = c("aut", "cre")))
Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <arXiv:0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.
ggplot2, gridExtra, knitr, Matrix, rmarkdown, SeuratObject,
usethis
H. Robert Frost [aut],
Azka Javaid [aut, cre]