Packaged: 2022-05-14 15:19:14 UTC; Sebastian Weirich

Depends: R (>= 4.1), survey (>= 4.1-1), BIFIEsurvey, progress, lavaan
Packaged: 2022-06-29 11:37:58 UTC; Sebastian Weirich

0.7.3), eatGADS (>= 0.20.0), janitor, msm, lme4, utils
Packaged: 2022-07-07 09:23:22 UTC; Sebastian Weirich

0.7.4), eatGADS (>= 0.20.0), janitor, msm, lme4, utils,
person("Karoline", "Sachse", role = c("aut")),
Packaged: 2022-11-14 16:57:06 UTC; Sebastian Weirich

Title: Miscellaneous Functions for the Analysis of Educational
person("Benjamin", "Becker", role = c("aut")),
person("Nicole", "Mahler", role = c("aut")))
Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive
analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep',
'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN').
The function for defining (weighted) contrasts in weighted effect coding refers to
Functions for weighted statistics refer to
Packaged: 2020-09-10 10:47:16 UTC; Sebastian Weirich

Packaged: 2020-10-19 14:04:21 UTC; Sebastian Weirich

Packaged: 2021-02-09 10:21:51 UTC; Sebastian Weirich

Packaged: 2021-07-06 14:52:14 UTC; Sebastian Weirich

Packaged: 2021-08-11 08:40:37 UTC; Sebastian Weirich

Packaged: 2021-11-10 11:00:58 UTC; Sebastian Weirich

Packaged: 2022-05-05 10:31:00 UTC; Sebastian Weirich

Packaged: 2022-06-28 13:54:36 UTC; Sebastian Weirich

Packaged: 2022-11-10 15:45:45 UTC; Sebastian Weirich

Title: Genotyping using Next Generation Sequencing Data
Author: Na You <youn@mail.sysu.edu.cn> and Gongyi Huang<huanggyi@mail2.sysu.edu.cn>
Description: Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Packaged: 2015-05-05 13:11:43 UTC; huanggongyi