fn_cov_mle: Wrapper for 'obj_offspring_reparam'.
In dcgerard/updogAlpha: Using Parental Data for Offspring Genotyping

This exists to calculate the Hessian of the parameters at the end of updog_update_all.

fn_cov_mle(par, ocounts, osize, ploidy, prob_geno, p1counts = NULL,
  p1size = NULL, p2counts = NULL, p2size = NULL, p1geno = NULL,
  p2geno = NULL, bias_val_mean = 0, bias_val_sd = 0.7,
  seq_error_mean = -4.7, seq_error_sd = 1, model = c("f1", "s1", "hw",
  "uniform"))

`par`	A numeric vector of length 4. The elements are `s`, `ell`, `r`, and the logit of `out_prop` from `obj_offspring_reparam`.
`ocounts`	The observed counts of the refernce allele for each individual.
`osize`	The observed number of reads for each individuals.
`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`.
`p1counts`	A vector of non-negative integers. The ith element is the number of reads of the reference allele in the ith sample of parent 1. If `NULL` then the prior probabilities on parent 1's genotype will default to uniform.
`p1size`	A vector of positive integers. The ith element is the total number of reads in the ith sample of parent 1. If `NULL` then the prior probabilities on parent 1's genotype will default to uniform.
`p2counts`	A vector of non-negative integers. The ith element is the number of reads of the reference allele in the ith sample of parent 2. If `NULL` then the prior probabilities on parent 2's genotype will default to uniform.
`p2size`	A vector of positive integers. The ith element is the total number of reads in the ith sample of parent 2. If `NULL` then the prior probabilities on parent 2's genotype will default to uniform.
`p1geno`	The initial value of the first parental genotype.
`p2geno`	The initial value of the second parental genotype.
`bias_val_mean`	The prior mean on the log of `bias_val` (corresponding to `parvec[1]`). Set `bias_val_sd = Inf` to have no penalty on the bias parameter.
`bias_val_sd`	The prior standard deviation on the log of `bias_val` (corresponding to `parvec[1]`). Set `bias_val_sd = Inf` to have no penalty on the bias parameter.
`seq_error_mean`	The mean of the logit-normal prior on the sequencing error rate, which corresponds to `parvec[2]`. Set `seq_error_sd = Inf` to have no penalty on the sequencing error rate. The default is -4.7, which roughly corresponds to a mean sequencing error value of 0.009. If you want to constain `seq_error` to be zero, you need to set `update_seq_error = FALSE`, `seq_error = 0`, and`seq_error_mean = -Inf`.
`seq_error_sd`	The standard deviation of the logit-normal prior on the sequencing error rate, which corresponds to `parvec[2]`. The default is 1, which at three standard deviations is about a sequencing error rate of 0.15. Set `seq_error_sd = Inf` to have no penalty on the sequencing error rate.
`model`	The model for the genotype distribution. Do we assume an F1 population (`"f1"`), an S1 population (`"s1"`), Hardy-Weinberg equilibrium (`"hw"`), or a uniform distribution (`"uniform"`).