## All functions

add_intercepts()

Add intercepts to tidied log-likelihood tibble

add_phenames()

Replace old (default) trait names with true trait names

add_pmap()

Add physical map contents to tibble

assemble_profile_tib()

Assemble a profile log-likelihood tibble

boot_pvl()

Perform bootstrap sampling and calculate test statistics for each bootstrap sample

calc_Bhat()

Calculate estimated allele effects, B matrix

calc_Sigma()

Calculate the phenotypes covariance matrix Sigma

calc_covs()

Calculate Vg and Ve from d-variate phenotype and kinship

calc_invsqrt_mat()

Calculate matrix inverse square root for a covariance matrix

calc_lrt_tib()

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

calc_sqrt_mat()

Calculate matrix square root for a covariance matrix

check_identical()

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

check_missingness()

Check for missingness in phenotypes or covariates

find_pleio_peak_tib()

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

fit1_pvl()

Fit a model for a specified d-tuple of markers

get_effects()

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

make_id2keep()

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

plot_pvl()

Plot tidied results of a pvl scan

prep_X_list()

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

prep_mytab()

Prepare mytab object for use within scan_pvl R code

qtl2pleio

qtl2pleio.

rcpp_calc_Bhat()

Estimate allele effects matrix, B hat, with Rcpp functions

rcpp_calc_Bhat2()

Estimate allele effects matrix, B hat, with Rcpp functions

rcpp_log_dmvnorm2()

Calculate log likelihood for a multivariate normal

scan_pvl()

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

sim1()

Simulate a single data set consisting of n subjects and 2 phenotypes for each

sim400()

Simulate many bivariate phenotype files and write them to a new directory

subset_input()

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

subset_kinship()

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

tidy_scan_pvl()

Tidy the data frame outputted by scan_pvl for further analysis & plotting

transform_loglik_mat()

Assemble tibble from matrix of log-likelihood values