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tipr (development version)

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