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@(@\newcommand{\B}[1]{ {\bf #1} } \newcommand{\R}[1]{ {\rm #1} } \newcommand{\W}[1]{ \; #1 \; }@)@This is dismod_at-20221105 documentation: Here is a link to its current documentation .
Bound The Covariate Multiplier Absolute Data Effect

Syntax
Purpose
database
max_abs_effect
covariate_name
bnd_mulcov_table
     max_cov_diff
     max_mulcov
Infinite Case
Example

Syntax
dismod_at database bnd_mulcov max_abs_effect
 dismod_at database bnd_mulcov max_abs_effect covariate_name
Purpose
This command is used to set the maximum absolute effect in the model for the data values. This is done by changing the lower and upper bounds for the covariate multipliers (ignoring bounds in the corresponding priors). The meas_noise multipliers and subgroup covariate multipliers are not included. The subgroup covariate multipliers are random effects and bound_random set the absolute bound for all the random effects.

database
Is an http://www.sqlite.org/sqlite/ database containing the dismod_at input tables which are not modified.

max_abs_effect
is either inf (for infinity) or a non-negative value that bounds absolute covariate effects. A covariate multiplier is defined by each row of the mulcov_table . We use the notation mul_value for a value of the multiplier. We use the notation cov_value for a value of the covariate in the data table. We use the notation cov_ref for the reference for the covariate. The maximum effect condition is
   | mul_value * (cov_value - cov_ref) | <= max_abs_effect
 Note that the limits on the covariate multiplier in its prior have units and the max_abs_effect does not have units.

covariate_name
If this argument is present, it is a covariate_name . In this case, the inequality above only refers to covariate multipliers that use this covariate.

bnd_mulcov_table
The table bnd_mulcov_table is an input and output for this command.

max_cov_diff
The max_cov_diff column is not changed.

max_mulcov
The max_mulcov column is set so the inequality above is true for all the data that is modeled using this covariate multiplier and that is included in the fit. To be specific, for each covariate multiplier
   max_mulcov = max_abs_effect / max_cov_diff
 If covariate_name is present, max_mulcov the bound is only changed for multipliers that use that covariate. The max_mulcov value for meas_noise covariates are not changed.

Infinite Case
The case where max_abs_effect is inf or max_cov_diff is zero, max_mulcov is set to null (which corresponds to plus infinity).

Example
The file user_bnd_mulcov.py contains an example and test using this command.
Input File: devel/cmd/bnd_mulcov_command.cpp