All functions

add_intercepts()

Add intercepts to tidied loglikelihood 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 loglikelihood 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 dvariate 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 dtuple 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 loglikelihood values 