Changes in version 0.7.2 - Added cbc_suggest_priors() function to make it easier to get a starting point for priors based on attribute levels in the profiles object. Changes in version 0.7.1 (2025-10-20) - Auto-encoding to dummy coding occurs for cbc_choices and cbc_power if the design is detected to have a no_choice option. - Updated some of the print methods so that they don't error if the choices or design objects are modified by dplyr functions. Changes in version 0.7.0 - Re-configures the design encoding to use "standard" coding by default. - Reasoning for standard coding by default is for easier interpretation of summary metrics like balance and overlap. - New function cbc_encode() used to convert designs to dummy or effects coding. Changes in version 0.6.4 (2025-09-23) - Adds balance_by argument to force balanced sampling in designs with restricted or otherwise unbalanced levels across attributes. - Fixes issue where remove_dominant was not working if there was a no_choice option. Changes in version 0.6.3 (2025-08-21) - Improve the greedy methods to include proper handling of the dominance checking and overall efficiency improvements. Changes in version 0.6.1 - Added include_probs argument to cbc_design(), which includes predicted choice probabilities in the returned design data frame if include_probs = TRUE. Defaults to FALSE. Changes in version 0.6.0 - Major overhaul of the package with breaking changes. - New function, `cbc_priors()``. This allows users to specify a set of priors according to a wide variety of model specifications, including random parameters (with or without correlated heterogeneity), interactions, and "no choice" options. These priors can then be used to create designs and simulate choices. - Coefficients for levels of an attribute in cbc_priors() can be named vectors, addressing #24. - Major overhaul of the cbc_design() function, with entirely new algorithms for searching for designs - One is "random", three are frequency-based ("greedy") algorithms, and three more are d-error minimizing algorithms. - Old methods removed: "full", "orthogonal", "dopt", "CEA", and "Modfed" - Bayesian D-efficient designs are now created based on the priors provided. With random parameters in the priors, a Bayesian D-efficient design will be created. - New support for removing dominant alternatives from designs. - New support for randomizing the order of questions and alternatives across respondents, addresses #29. - New cbc_inspect() function for comprehensively inspecting designs. - New cbc_compare() function for comparing designs. - New functionality in cbc_power() for computing visualizing power analyses. Changes in version 0.5.2 - Bug fix in checking input settings (#34) Changes in version 0.5.1 - Patch to fix a joining issue in the join_profiles() function (#27) Changes in version 0.5.0 (2023-07-12) - Further revisions to the method argument in the cbc_design() function. - Added the "random" and "dopt" methods. - Added restrictions so that orthogonal designs cannot use the label argument or restricted profile sets (as either of these would result in a non-orthogonal design). Changes in version 0.4.0 (2023-06-30) - Adjustments made to the method argument in the cbc_design() function in preparation for potentially adding new design methods. - Added the "orthogonal" option for generating orthogonal designs. Changes in version 0.3.4 (2023-06-13) - Another small bug fix in cbc_design() related to #16 where factor level ordering for categorical variables were being mis-ordered. - Updated how the method argument is handled by default in cbc_design() to be more flexible (anticipating other methods in the future). - Added keep_db_error arg to cbc_design(). Changes in version 0.3.3 (2023-06-02) - Bug fix in cbc_design() where factor level ordering for categorical variables were being mis-ordered. - Added additional input check for appropriate priors in cbc_design(). Changes in version 0.3.2 (2023-05-23) - Modify how restrictions are defined in the cbc_restrict() function to allow users to provide expressions. Changes in version 0.3.1 - Add cbc_restrict() function to improve UI for adding restrictions to profiles. - Remove previous approach to including restrictions in cbc_profiles(). - Add new test cases Changes in version 0.3.0 (2023-05-09) - Bug fix: modify code in cbc_design() to avoid duplicate choice sets for the same respondents; addresses #7. - Bug fix: modify code in cbc_design() to allow Bayesian D-efficient designs with restricted profile sets; addresses #10 and #9. Changes in version 0.2.0 (2023-02-28) - Added a startup message when the package is loaded. - Updates for compatibility with logitr version 1.0.1. - Updated DESCRIPTION and CITATION to remove redundancy in title. - Updated documentation of returned values in several functions. Changes in version 0.1.0 - Added initial integration with {idefix} packages for Bayesian D-efficient designs Changes in version 0.0.5 - Updates for compatibility with logitr version 0.8.0. Changes in version 0.0.4 - Updates for compatibility with logitr version 0.7.0. Changes in version 0.0.3 - Modified the argument of cbc_profiles() to ... so that the user no longer needs to create a separate list to define the attributes and levels. - Modified the arguments for the randN() and randLN() functions to mean and sd. - Improved printing of counts in cbc_balance() and cbc_overlap(). - Updated names of random parameter models to match that of future logitr v0.6.0. - Updated documentation and examples for all functions. - Adding piping example to readme. Changes in version 0.0.2 - Added support for conditional levels in cbc_profiles() Changes in version 0.0.1 - Added a NEWS.md file to track changes to the package.