obj_offspring_weights: Same thing as 'obj_offspring', but each sample's log-density...
In dcgerard/updogAlpha: Using Parental Data for Offspring Genotyping

This is mostly used in the EM algorithm.

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obj_offspring_weights(ocounts, osize, weight_vec, ploidy, prob_geno,
  bias_val = 1, seq_error = 0, od_param = 0, outlier = FALSE,
  out_prop = 0.01, out_mean = 0.5, out_disp = 1/3)

`ocounts`	The observed counts of the refernce allele for each individual.
`osize`	The observed number of reads for each individuals.
`weight_vec`	A vector of numerics between 0 and 1. They don't have to sum to 1.
`ploidy`	An integer. The ploidy of the species. This is assumed to be the same for each individual.
`prob_geno`	The allele frequencies of the genotypes. See `get_prob_geno`.
`bias_val`	The bias parameter. A value of 1 means there is no bias toward one allele or the other. A value less than one indicates a bias toward the reference allele. A value greater than one indicates a bias toward the non-reference allele.
`seq_error`	The sequencing error rate.
`od_param`	The overdispersion parameter in the beta-binomial model for the OK counts. When this is zero, this resorts to the binomial model for counts.
`outlier`	A logical. Should we include an outlier model (`TRUE`) or not (`FALSE`)? Defaults to `FALSE`.
`out_prop`	The proportion of points that are outliers. Defaults (quite arbitrarily) to `0.01`.
`out_mean`	The mean of beta-binomial for the outlier distribution. Defaults to `0.5`.
`out_disp`	The overdispersion parameter of the outlier distribution. Defaults to `1/3`, which corresponds to a uniform distribution for the underlying beta when `out_mean = 0.5`.