Title: Design and Analyze Studies using the Sequential Parallel
Description: 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,

Title: Compute Power or Sample Size for GWAS with Covariate Effect
Description: 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>.
Packaged: 2022-03-31 15:01:42 UTC; ziangzhang

Packaged: 2022-09-05 20:42:25 UTC; ziangzhang

Title: Statistical package for species richness estimation
Description: SPECIES is an R package for estimation of species richness

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

Title: Receptor Abundance Estimation using Reduced Rank Reconstruction
person("Azka", "Javaid", email = "azka.javaid.gr@dartmouth.edu", role = c("aut", "cre")))
Description: 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>.
Suggests: ggplot2, gridExtra, knitr, Matrix, rmarkdown, SeuratObject,
Imports: Ckmeans.1d.dp, magrittr, rsvd, Seurat
Packaged: 2022-10-11 14:11:44 UTC; azkajavaid