tipr 1.0.2

• adjust_coef_with_binary() now assumes the coefficient is from a linear model rather than loglinear. Use loglinear = TRUE to get the old behavior. (#12, @malcolmbarrett)
• Fixed roxygen issue with package documentation
• Update messaging and errors

tipr 1.0.1

• Fixed bug, functions based on the adjust_coef_with_binary function had the old parameter names (exposed_p and unexposed_p). These were changed to match the other new updates from version 1.0.0 to now be exposed_confounder_prev and unexposed_confounder_prev.
• Change “relative risk” to “risk ratio” in all documentation.
• Add new JOSS citation

tipr 1.0.0

Breaking changes. The names of several arguments were changed for increased clarity:

• effect -> effect_observed
• outcome_association -> confounder_outcome_effect
• smd -> exposure_confounder_effect
• exposed_p -> exposed_confounder_prev
• unexposed_p -> unexposed_confounder_prev
• exposure_r2 -> confounder_exposure_r2

• outcome_r2 -> confounder_outcome_r2

• Added two new example datasets: exdata_continuous and exdata_rr

tipr 0.4.2

• Make the output tibble names consistent (adjusted_effect -> effect_adjusted)

tipr 0.4.1

• Add additional functions that specify *_with_continuous() (long form of, the function names, the default unmeasured confounder is Normally distributed)
• Change tip_lm() to tip_coef().

tipr 0.4.0

• Changed the name of lm_tip() to tip_lm()
• The API has been fundamentally updated so that the functions now take a numeric value as a first argument rather than a data frame.
• Added adjust_* functions to allow for specification of all unmeasured confounder qualities without tipping
• Split tip_* functions into hazard ratio, odds ratio, and relative risk
• Add R2 parameterization with tip_coef_with_r2(), adjust_coef_with_r2(), and r_value()

tipr 0.3.0

• Added ability to perform sensitivity analyses on linear models via lm_tip()

tipr 0.2.0

• Updated several function and parameter names. The main functions are now tip() and tip_with_binary(). The parameter names are more self-explanatory.
• The API has been fundamentally updated so that the functions now take a data frame as a first argument.
• There is now explicit (but not required) integration with the broom package.

tipr 0.1.1

• initial CRAN release