- Fixed discrepnacy in accordance with CRAN standards: Removed global variables
`estimate`

and`chemical`

from enviornment in`plot.wqs()`

.

- Fixed discrepancies with CRAN standards:
- DESCRIPTION FILE: Rewrote dois to match CRAN standards (<doi:…>, not <:…>)
`impute.multivariate.bayesian()`

: Removed empty details section

- Additional Resource: A methods paper (Hargarten & Wheeler (2020) <:10.1016/j.envres.2020.109466>) is cited in the README, DESCRIPTION, and some function files. This paper provides theoretical details in using the package.
`estimate.wqs()`

: Can now compare training and validation datasets, which is commonly done in WQS analysis.`plot.wqs()`

: Removed defunct`filename`

argument. Plots are no longer saved automatically; please save manually using ().

- New
`impute.multivariate.regress()`

: imputes all chemicals jointly using a multivariate Bayesian regression.

- Documentation clarity and edits
`do.many.wqs()`

:- Fixed error in collecting WQS results. When training proportion not 1, WQS is smaller than matrix to hold values.
- Added a message that included the sample size, number of chemicals, datasets to impute, and number of covariates modelled.
- Passed the explicit
`B`

argument to the`...`

argument. Should not impact any code, as it is called within`estimate.wqs()`

.

`estimate.wqs()`

:- Cleaned up code
- Updated accessories
`summarize.compare()`

and`make.descriptive.tables()`

.

`impute.sub()`

: replaced for-loop with much faster`tidyr::replace_na()`

.`impute.boot()`

and`impute.Lubin()`

: Now uses the survival routines that have been updated in version 3.`plot.wqs()`

:- Now uses
`tidyr::pivot_longer()`

instead of`tidyr::gather()`

in response to update in**tidyr**package from v. 0.8 to v. 1.0.0. See`vignette("pivot")`

and`vignette("in-packages")`

in tidyr documentation for more information. - Updated documentation to require tidyr to be v. 1.0.0.

- Added a
`ggplot2`

layer to make x-axis smaller so you can read labels on weights histogram

- Now uses
- Internal Improvements
- accessory
`check_constants()`

: Fixed bug that returns error if the number of bootstraps (B), imputed datasets (K), or length of chain (T) is 0 - new accessory
`imp_cond_MVN`

: An accessory function used to impute conditional truncated multivariate normal used in`impute.multivariate.regress()`

. Added**condMVNorm**&**tmvtnorm**packages to DESCRIPTION. - accessory
`formatMedianIQR`

: Changed format of median and IQR to “median[Q1, Q3]”. - accessory
`is.even()`

,`is.odd()`

– now accepts tolerance argument for consistency with other`is...`

functions. - accessory
`specify.init()`

: change argument to C. - accessory
`replace_na()`

: Changes made in`impute.sub()`

- accessory

- New
`estimate.wqs.formula()`

now has a formula capability. Can type in a full formula and specify column names instead of dividing up the data into three columns. `impute.Lubin()`

:- Softly deprecated. Use
`impute.boot`

instead. - If chemcol is complete, the function NOW returns complete data with warning
- The
`bootstrap_index`

element is now factors instead of numbers. Easier to see what subjects were selected.

- Softly deprecated. Use
`pool.mi()`

:- Added argument
`prt`

to print to standard output so that the`pool.mi`

object can be read in an understandable fashion. - Argument
`methods`

is NOW more robust by adding`tolower()`

. Now, if someone writes in all caps, the function still works. - Accessory
`mice.df()`

is now clearer – only accepts either method to avoid mistake.

- Added argument

- Documentation & example clarity.
- Moved the base
**R**packages from`IMPORTS`

to`DEPENDS`

for clarity. As the base R packages are already attached, this should have no impact on package performance. - Replace all checks using
`class(.)==matrix`

with`is()`

, as directed from CRAN. - Clarified internal performance
- accessory
`check_constants()`

: Used`sprintf()`

for more generic error returns. - accessory
`check_imputation`

: Removed checking Z here. Now Z is checked in each`impute.`

.. function. - accessory
`head.array()`

: made package for`head()`

clear by using`utils::head()`

. - accessory
`is.even()`

,`is.odd()`

– now accepts tolerance argument for consistency with other`is.`

.. functions.

- accessory
`combine.AIC()`

: removed comma from output that was generated from format.mean.sd() (Added July 22, 2019)`estimate.wqs()`

:- Syntax clarification,
- Added
`if/else`

on signal function options for speed. - Now uses
`check_wqs_function()`

; deleted duplicate check_function() from before.

`impute.boot()`

:- Added note to contact Jay Lubin for SAS macro
- Cleaned up code/spaces. Didn’t change functionality.

- The
`plot.wqs()`

no longer automatically saves the plots to reduce clutter and save the user’s workspace. However, you can still use`ggsave()`

on the output if you wish to save WQS plots. If you depended on this behavior, you’ll need to condition on`packageVersion("miWQS") > "0.0.0"`

. - The title of the package is changed from “analysis” to “regression” to make the name more specific.
- Creates a README vignette.
- Shortens description in DESCRIPTION file for clarity.

- New
`analyze.individually()`

does individual chemical analysis. The outcome is independently regressed on each chemical. Individual chemical analyses with the outcome can be used to determine whether the mixture of chemicals is positively or negatively related to the outcome (the`b1.pos`

argument in`estimate.wqs()`

). - New
`do.many.wqs()`

does many WQS analyses, which is useful in the second stage of multiple imputation. - New
`combine.AIC()`

combines AIC results from many WQS analyses, similar in spirit to`pool.mi()`

. - New
`impute.boot()`

performs bootstrapping imputation for many chemicals, not just one. - New
`impute.sub()`

imputes the values below the detection limit with 1/sqrt(2) of that’s chemical’s detection limit.

- Documentation is clarified and keywords are added.
- Most
`cat()`

used in the functions are changed to messages`message()`

so that the user can suppress messages using`suppressMessage()`

. - Consistent comments in all functions now use notation
`#>`

. - The
`estimate.wqs()`

- Arguments
`B`

argument: Documentation is clarified when bootstrapping within WQS. You now can do WQS regression without bootstrapping, but it is not recommended.`place.bdls.in.Q1`

argument now does something. You can set it to FALSE and regular quantiles are made. Passed to`make.quantile.matrix()`

.`offset`

argument fixes transfer to`glm2()`

. User should enter the offset on the normal scale; the logarithm is not taken. It is passed to`glm2()`

and`glm()`

. FROM: The`offset`

argument in`glm2()`

has a default value of 0. The offset value in`estimate.wqs()`

by default is a vector of 1’s. NOW: When using`glm2()`

, offset argument now takes the logarithm as expected in all instances, (especially in`wqs.fit()`

). User does not change the default of offset value.

- Inner Mechanics
- Removes duplicate output in by adding the
`suppressMessages()`

function. - Uses
`wqs.fit()`

accessory function instead of code in`estimate.wqs()`

. - Changes lower case “c” to upper case “C” to avoid conflict with
`c()`

.

- Removes duplicate output in by adding the

- Arguments
- The
`impute.univariate.bayesian.mi()`

function:`T`

argument: changes default length of chain to 1,000 in order to be consistent with other functions.`indicator.miss`

output now returns as a single number (a sum) rather than a vector.- Fixes bug in initial values for the standard deviation in MCMC chain. Calculates standard deviation using the logarithm of
`cov()`

function. FROM: The`complete.observations`

argument was used for observed X. NOW: standard deviations are calculated based on the substituted imputed chemical matrix, X, as covariances may not exist if X has many missing values. - Fixes bug so that the imputed values now draw from the last state, instead of the second-to-last state.
- Inner Mechanics
- Reduced # of objects to be used in finding
`initial2`

. - Changed object name
`x.miss.initial`

to`log.x.miss.initial`

. - Examples still remain the same.

- Reduced # of objects to be used in finding

- The
`make.quantile.matrix()`

:- Adds the
`place.bdls.in.Q1`

argument. The default automatically places any missing values in X placed into the first quantile. We suggest the user does not specify this argument unless the user wants to be specific in how missing values in components are handled. In version 0.0.9, this argument previously had no effect on how quantiles were made. This argument now has an effect. - Fixes an error if there are ties in the quantiles. An error occurred in
`cut.default(...): 'breaks' are not unique`

. We use the`.bincode()`

function instead so that ties are handled if they arise.

- Adds the
- The
`print.wqs()`

now concatenates, instead of prints, the convergence of the bootstrap samples. - The
`simdata87`

data:- element
`Z`

contains renamed covariate names for clarity. - elements
`X.true, X.bdl, DL, delta, n0`

: all chemical names are converted to plain text for clarity to be used in R. The “p-p`” are removed, and the “alpha” and “gamma” are spelled out directly.

- element

- First Release of Package to the public.
- For updates to CRAN team, see cran-comments.
- Replaces examples using example dataset in package instead of using package wqs. Looks cleaner
- Removes printed output from
`estimate.wqs()`

. - Makes documentation from
`estimate.wqs()`

clearer. - Cleans
`print.wqs()`

documentation

- Reworked
`plot.wqs()`

function by using**ggplot2**instead of base plotting in R.

- Fixes bug in doing Poisson Rate WQS regressions. Adds argument offset to the
`check_function()`

and`randomize.train()`

. - For updates to CRAN team, see cran-comments.

- Adds a
`NEWS.md`

file to track changes to the package. - First Release of the Package to CRAN team
- Successfully passed windows check.