DNA methylation signatures are usually based on multivariate
    approaches that require hundreds of sites for predictions.  'CimpleG'
    is a method for the detection of small CpG methylation signatures used
    for cell-type classification and deconvolution.  'CimpleG' is time
    efficient and performs as well as top performing methods for cell-type
    classification of blood cells and other somatic cells, while basing
    its prediction on a single DNA methylation site per cell type (but
    users can also select more sites if they so wish).  Users can train
    cell type classifiers ('CimpleG' based, and others) and directly apply
    these in a deconvolution of cell mixes context.  Altogether, 'CimpleG'
    provides a complete computational framework for the delineation of
    DNAm signatures and cellular deconvolution.  For more details see Maié
    et al. (2023) <doi:10.1186/s13059-023-03000-0>.

Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. <doi:10.1177/0962280220921909>.

Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data
  analyses. This package combines different packages to have an ultimate package
  for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 
  'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.

Immune related gene sets provided along with the 'cinaR' package.

Function using lemmatization to classify educational programs according to the CINE(Classification International Normalized of Education) for Peru.

Method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.