Minor: indent and doc code blocks
Separate the [mean, RMS, normalize] functions
Make debug printing conditional on debug flag
Use saved values as inits

Saved pickles correspond to maximum posterior probability estimates.
Prior of s: include further options, modify uniform.
Max posterior probability results as txt table

Max posterior probability runs
- use the uniform prior for s
- Ihp name is the one the latent x's converged to
- the number is the log posterior probability
- For each Ihp the best model is saved (commands as comments)
Ensure that the method returns a copy of the matrix

instead of modifying in place.
Fix the usage of tau

- Tau used to be vector, now matrix with several (NIhp) identical
columns.
- in the resids_normal Normal the usage of tau.value was a mistake, now
the new matrix tau is used instead.
Use min-max stretched Ihp data as x inits

Also include table where the latent x converged with what posterior log
probability
Minor: change triple quote type
Compute 'Maximum A Posteriori' (MAP) estimates
Using rpy2 read in artesunate CRC data, compute medians
Save MAP x estimates into the main data.frame
'PerformanceAnalytics'-type scatterplot of Ihps and the latent x
Add Group to the 'PerformanceAnalytics'-type scatterplot
Print names of the variables included in [the 'PerformanceAnalytics' scatterplot]
T-test on (latent x MAP estimates) between the two treatment groups
T-test on log(latent x MAP estimates) between the two treatment groups
Use 'PerformanceAnalytics' for the s correlation plots, too
Compute required thin multiplier