# multibias 1.7.2

* Added `multibias_plot()` to visualize sensitivity analysis results
* When using validation data in `multibias_adjust()` the function now
incorporates uncertainty of the effect estimates from the validation data
by sampling from each estimate's mean and SE. Now, when using validation data,
the confidence intervals from multibias bootstrapped results will represent
two sources of uncertainty: random error and systematic error.
* Added FAQ documentation

# multibias 1.7.1

* Updated code with dynamic formula construction so that there is no limit
to the number of known confounders one can include when using `bias_params`
as an input for `multibias_adjust()`
* `multibias_adjust()` now has built in bootstrapping
* Added `summary()` method to `data_observed`

# multibias 1.7

* Created `bias_params` class to handle bias parameter inputs to
`multibias_adjust()`
* Replaced the various `adjust()` functions with a single `multibias_adjust()`
function. Users now specify the biases they want to adjust for in the
`data_observed` object. Bias adjustment formulas are now found in the
`bias_params` documentation.
* The user now specifies biases for adjustment in the `bias` input of
`data_observed`
* Removed `evans` data; now only used in vignette

# multibias 1.6.3

* Created a `pkgdown` web page: www.paulbrendel.com/multibias
* Refined the vignette, including a new NHANES analysis

# multibias 1.6.2

* The following functions now accept `data_validation` as an input for bias
  adjustment:
  * `adjust_om_sel.R`
  * `adjust_uc_sel.R`
  * `adjust_uc_em.R`
  * `adjust_uc_om.R`
  * `adjust_uc_em_sel.R`
  * `adjust_uc_om_sel.R`

# multibias 1.6.1

* The following functions now accept `data_validation` as an input for bias
  adjustment:
  * `adjust_em_om.R`
  * `adjust_em_sel.R`
* Bug fixes for validation data input in `adjust_em.R` and `adjust_om.R`
* Bug fixes for data and printing in `data_observed` and `data_validation`

# multibias 1.6

* Created new class `data_observed` to represent observed causal data
* All `adjust` functions now take `data_observed` as input
* Created new class `data_validation` to represent causal data that can be used
  as validaiton data for bias adjustment
* The following functions now accept `data_validation` as an input for bias
  adjustment:
  * `adjust_uc.R`
  * `adjust_em.R`
  * `adjust_om.R`
  * `adjust_sel.R`

# multibias 1.5.3

* All exposure misclassificaiton naming changed from  `emc` changed to `em`
* All outcome misclassificaiton naming changed from  `omc` changed to `om`
* Added lifecycle badges for above function renames
* Merged `adjust_multinom_uc_em_sel` into `adjust_uc_em_sel`
* Merged `adjust_multinom_uc_om_sel` into `adjust_uc_om_sel`
* The following functions now support more flexible combinations of continuous
  and binary exposure-outcome variables:
  * `adjust_uc_em_sel.R`
  * `adjust_uc_om_sel.R`

# multibias 1.5.2

* Merged `adjust_multinom_emc_omc` into `adjust_emc_omc`
* Merged `adjust_multinom_uc_emc` into `adjust_uc_emc`
* Merged `adjust_multinom_uc_omc` into `adjust_uc_omc`
* The following functions now support more flexible combinations of continuous
  and binary exposure-outcome variables:
  * `adjust_emc_sel` (exposure must be binary)
  * `adjust_omc_sel` (outcome must be binary)
  * `adjust_uc_emc` (exposure must be binary)
  * `adjust_uc_omc` (outcome must be binary)
  * `adjust_multinom_uc_emc` (exposure must be binary)
  * `adjust_multinom_uc_omc` (outcome must be binary)
* Expanded the number of known confounders in dataframes:
  * `df_omc_sel`
  * `df_omc_sel_source`

# multibias 1.5.1

* The following functions now support more flexible combinations of continuous
  and binary exposure-outcome variables:
  * `adjust_uc`
  * `adjust_emc` (exposure must be binary)
  * `adjust_omc` (outcome must be binary)
  * `adjust_sel`
  * `adjust_uc_sel`
* Expanded the number of known confounders in dataframes:
  * `df_uc_omc`
  * `df_uc_omc_source`
  * `df_uc_emc`
  * `df_uc_emc_source`
* Dataframes `df_uc` and `df_uc_source` now both have continuous and
  binary exposures and outcomes.

# multibias 1.5.0

## New features

* Added two functions for simultaneous adjustment of uncontrolled confounding,
  outcome misclassification, and selection bias: `adjust_uc_omc_sel` &
  `adjust_multinom_uc_omc_sel`.
* Added dataframes with uncontrolled confounding, outcome misclassification,
  and selection bias: `df_uc_omc_sel` and `df_uc_omc_sel_source`.
* Expanded the number of known confounders in dataframes:
  * `df_uc_sel`
  * `df_uc_sel_source`

# multibias 1.4.0

## New features

* Added two functions for simultaneous adjustment of exposure misclassification
  and outcome misclassification: `adjust_emc_omc` & `adjust_multinom_emc_omc`.
* Added dataframes with exposure misclassification and outcome
  misclassification: `df_emc_omc` and `df_emc_omc_source`.
* Expanded the number of known confounders in dataframes:
  * `df_emc_sel`
  * `df_emc_sel_source`

## Bug fixes

* Improved some of the documentation of equations.

# multibias 1.3.0

## New features

* Added a function for simultaneous adjustment of outcome misclassification
  and selection bias: `adjust_omc_sel`.
* Added dataframes with outcome misclassification and selection bias:
  `df_omc_sel` and `df_omc_sel_source`.
* Expanded the number of known confounders in dataframes:
  * `df_uc`
  * `df_uc_source`
  * `df_emc`
  * `df_emc_source`
  * `df_omc`
  * `df_omc_source`
  * `df_sel`
  * `df_sel_source`

## Bug fixes

* Fixed bug in `adjust_omc` that appears when using three confounders

# multibias 1.2.1

* Moved examples from README to vignette.

# multibias 1.2.0

## New features

* Added two functions for simultaneous adjustment of uncontrolled confounding
  and outcome misclassification: `adjust_uc_omc` and `adjust_multinom_uc_omc`.
* Added dataframes with uncontrolled confounding and outcome misclassification:
  `df_uc_omc` and `df_uc_omc_source`.

## Bug fixes

* None

# multibias 1.1.0

## New features

* Created new function to adjust for outcome misclassification: `adjust_omc`.
* Added dataframes for all single bias scenarios:
  * `df_emc`
  * `df_emc_source`
  * `df_omc`
  * `df_omc_source`
  * `df_sel`
  * `df_sel_source`
  * `df_uc`
  * `df_uc_source`

## Bug fixes

* `adjust_sel` had been weighing with the probability of selection
  instead of the **inverse** probability of selection.

# multibias 1.0.0

* Initial CRAN submission.