We curated 147 of expression array, from 3 species(human,mouse,rat), 
              3 companies('Affymetrix','Illumina','Agilent'), 
              by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, 
              and then annotate them to genes.

Provides non-invasive annotation of package load calls 
    such as \code{library()}, \code{p_load()}, and \code{require()} so that we can have an idea of what 
    the packages we are loading are meant for.

Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions. Borstein & O'Meara (2018) <doi:10.7717/peerj.5179>.

Provides functions to create image annotations through polygon outlining. Annotator has the same function as 'graphics::locator()' but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the 'htmlwidgets' package and 'fabric.js' JavaScript library <http://fabricjs.com/>.

Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.

Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
    to compare clustering structures with applications in functional magnetic resonance imaging
    data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
    simultaneously and also to identify features that contribute to the differential clustering.

Analyses of frequencies can be performed using an alternative test based 
    on the G statistic. The test has similar type-I error rates and power as 
    the chi-square test. However, it is based on a total statistic that can be 
    decomposed in an additive fashion into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call 
    this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its 
    similarities with ANOVA. This framework also renders plots of frequencies 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. The ANOFA is a tool that assesses 
    the significance of effects instead of the significance of parameters; as such, 
    it is more intuitive to most researchers than alternative approaches based on 
    generalized linear models. 
    See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.