R.cache, broom, ggplot2, tibble, magrittr, tidyr, dplyr,
purrr, lubridate, highcharter, jsonlite, keyring, plotly,
htmlwidgets, utils, httr, methods, PTXQC

R.cache, broom, ggplot2, tibble, magrittr, tidyr, dplyr,
lubridate, highcharter, jsonlite, keyring, plotly, htmlwidgets,
utils, httr, methods, PTXQC

R.cache, broom, ggplot2, tibble, magrittr, tidyr, dplyr,
lubridate, highcharter, jsonlite, keyring, utils, httr,
methods, PTXQC
'KorAPConnection.R' 'KorAPCorpusStats.R'
'RKorAPClient-package.R' 'KorAPQuery.R' 'association-scores.R'
'ci.R' 'highcharter-helper.R' 'misc.R' 'reexports.R'

R.cache, broom, ggplot2, tibble, magrittr, tidyr, dplyr,
lubridate, highcharter, jsonlite, keyring, utils, httr,
methods, PTXQC, purrr, stringr, urltools
'KorAPConnection.R' 'KorAPCorpusStats.R'
'RKorAPClient-package.R' 'KorAPQuery.R' 'association-scores.R'
'ci.R' 'collocationAnalysis.R' 'collocationScoreQuery.R'
'hc_add_onclick_korap_search.R' 'hc_freq_by_year_ci.R' 'misc.R'
'reexports.R'

R.cache, broom, ggplot2, tibble, magrittr, tidyr, dplyr,
lubridate, highcharter, jsonlite, keyring, utils, httr, curl,
methods, PTXQC, purrr, stringr, urltools

A Genotype Calling Algorithm for Affymetrix SNP Arrays
Nusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong <wongg62@berkeley.edu>
A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.
Internal files Xba.CQV, Xba.regions (or other regions file)

Nusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong
<wongg62@berkeley.edu>
A classification algorithm, based on a multi-chip,
multi-SNP approach for Affymetrix SNP arrays. Using a large
training sample where the genotype labels are known, this
aglorithm will obtain more accurate classification results on
new data. RLMM is based on a robust, linear model and uses the
Mahalanobis distance for classification. The chip-to-chip
non-biological variation is removed through normalization. This
model-based algorithm captures the similarities across genotype
groups and probes, as well as thousands other SNPs for accurate
classification. NOTE: 100K-Xba only at for now.
Internal files Xba.CQV, Xba.regions (or other
regions file)