obj_wrapp_all: wrapper for 'obj_offspring_weights_reparam'
In dcgerard/updogAlpha: Using Parental Data for Offspring Genotyping

wrapper for obj_offspring_weights_reparam

obj_wrapp_all(parvec, ocounts, osize, weight_vec, ploidy, p1geno, p2geno,
  allele_freq = 0.5, p1counts = NULL, p1size = NULL, p1weight = NULL,
  p2counts = NULL, p2size = NULL, p2weight = NULL, bound_bias = FALSE,
  bound_od = FALSE, update_bias_val = TRUE, update_seq_error = TRUE,
  update_od_param = TRUE, seq_error_mean = -4.7, seq_error_sd = 1,
  bias_val_mean = 0, bias_val_sd = 0.7, model = c("f1", "s1", "hw",
  "uniform"), is_seq_ninf = FALSE)

`parvec`	A vector of three elements, s, ell, and r. We have s = log(bias_val) = log(d), ell = logit(seq_error) = logit(eps), and r = - logit(od_param) = - logit(tau).
`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 weights obtained via the E-step.
`ploidy`	An integer. The ploidy of the species. This is assumed to be the same for each individual.
`p1geno`	The genotype of parent 1 if `model = "f1"` or `model = "s1"`.
`p2geno`	The genotype of parent 2 if `model = "f1"`. This needs to be null if `model = "s1"`
`allele_freq`	The allele-frequency if `model = "hw"`
`p1counts`	The number of reference alleles observed from parent 1.
`p1size`	The number of reads observed from parent 1.
`p1weight`	The posterior probability that parent 1 is not an outlier.
`p2counts`	The number of reference alleles observed from parent 2.
`p2size`	The number of reads observed from parent 2.
`p2weight`	The posterior probability that parent 2 is not an outlier.
`bound_bias`	A logical. Should we bound the bias parameter `parvec[1]` by a somewhat arbitrary value (`TRUE`) or not (`FALSE`)?
`bound_od`	A logical. Should we bound the overdisperion parameter `parvec[3]` by a very small number (`TRUE`) or not (`FALSE`). This is mostly because my code for super super small values of `parvec[3]` can be unstable.
`update_bias_val`	A logical. Not used. Here for compatability with `grad_wrapp_all`.
`update_seq_error`	A logical. Not used. Here for compatability with `grad_wrapp_all`.
`update_od_param`	A logical. Not used. Here for compatability with `grad_wrapp_all`.
`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.
`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.
`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.
`model`	The model for the genotype distribution. Should we assume an F1 population (`"f1"`), an S1 population (`"s1"`), Hardy-Weinberg equilibrium (`"hw"`), or a uniform distribution (`"uniform"`)?
`is_seq_ninf`	Hacky way to get around fact that optim won't allow `-Inf` in `par`, even if `gr` will always be zero for that parameter. How dare they not anticipate my unique situation!