Using the DNA sequence and gene annotation files provided in 
            'ENSEMBL' <https://www.ensembl.org/index.html>, 
            the functions implemented in the package try to find the DNA sequences and 
            protein sequences of any given genomic loci, and to find the genomic coordinates 
            and protein sequences of any given protein locations, which are the frequent
            tasks in the analysis of genomic and proteomic data.

Calculates Agresti's generalized odds ratios.
  For a randomly selected pair of observations
  from two groups, calculates the odds that
  the second group will have a higher scoring outcome
  than that of the first group.
  Package provides hypothesis testing for if this odds
  ratio is significantly different to 1 (equal chance).

Evaluates likelihood ratio tests for alleged ancestry. Implements the methods of Tvedebrink et al (2018) <doi:10.1016/j.tpb.2017.12.004>.

Individual-based simulations forward in time,
    simulating how patterns in ancestry along the genome change after
    admixture. Full description can be found in Janzen (2021)
    <doi:10.1111/2041-210X.13612>.

Local structure in genomic data often induces dependence between observations taken at different genomic locations.  Ignoring this dependence leads to underestimation of the standard error of parameter estimates.  This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure(Cabrera et al (2012) <doi:10.1534/g3.112.002618>). All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.