## All functions

Add physical map contents to tibble

Perform bootstrap sampling and calculate test statistic for each bootstrap sample

Calculate estimated allele effects, B matrix

Calculate the phenotypes covariance matrix Sigma

Calculate Vg and Ve from d-variate phenotype and kinship

Calculate matrix inverse square root for a covariance matrix

Calculate a likelihood ratio test statistic from the output of scan_pvl()

Calculate profile lods for all traits

Calculate matrix square root for a covariance matrix

Check whether a vector, x, has all its entries equal to its first entry

Check for missingness in phenotypes or covariates

Convert scan_multi_oneqtl output of qtl2::scan1 output

Find the marker index corresponding to the peak of the pleiotropy trace in a tibble where the last column contains log likelihood values and the first d columns contain marker ids

Fit a model for a specified d-tuple of markers

Extract founder allele effects at a single marker from output of qtl2::scan1coef

Identify shared subject ids among all inputs: covariates, allele probabilities array, kinship, and phenotypes

Plot tidied results of a pvl scan

Create a list of component X matrices for input to stagger_mats, to ultimately create design matrix

Prepare mytab object for use within scan_pvl R code

Process inputs to scan functions

qtl2pleio.

Estimate allele effects matrix, B hat, with Rcpp functions

Estimate allele effects matrix, B hat, with Rcpp functions

Calculate log likelihood for a multivariate normal

Perform multivariate, one-QTL model fitting for markers on one chromosome

Perform multivariate, one-QTL model fitting for markers on all chromosomes

Permute the phenotypes matrix and then scan the genome. Record the genomewide greatest LOD score for each permuted data set.

Perform model fitting for all ordered pairs of markers in a genomic region of interest

Simulate a single multivariate data set consisting of n subjects and d phenotypes for each

Subset an input object - allele probabilities array or phenotypes matrix or covariates matrix. Kinship has its own subset function

Subset a kinship matrix to include only those subjects present in all inputs