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My R packge *powerlmm* 0.2.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.

# Changes in version 0.2.0

## New features

- Analytical power calculations now support using Satterthwaite’s degrees of

freedom approximation. `Simulate.plcp`

will now automatically create lme4 formulas if none is

supplied, see`?create_lmer_formula`

.- You can now choose what alpha level to use.
- Treat cluster sizes as a random variable,
`uneqal_clusters`

now accepts

a function indicating the distribution of cluster sizes, via the new argument

`func`

, e.g.`rpois`

or`rnorm`

could be used to draw cluster sizes. - Expected power for designs with parameters that are random variables,

can be calculated by averaging over multiple realizations, using the

argument`R`

. - Support for parallel computations on Microsoft Windows, and in GUIs/interactive

environments, using`parallel::makeCluster`

(PSOCK). Forking is still used for

non-interactive Unix environments.

## Improvements

- Calculations of the variance of the treatment effect is now much faster for

designs with unequal clusters and/or missing data, when cluster sizes are

large. The calculations now use the much faster implementation used by lme4. - Cleaner print-methods for
`plcp_multi`

-objects. - Multiple power calculations can no be performed in parallel, via the

argument`cores`

. `simulate.plcp_multi`

now have more options for saving intermediate results.`print.plcp_multi_power`

now has better support for subsetting via either [],

head(), or subset().

## Breaking changes

`icc_pre_subject`

is now defined as`(u_0^2 + v_0^2) / (u_0^2 + v_0^2 + error^2)`

,

instead of`(u_0^2) / (u_0^2 + v_0^2 + error^2)`

. This would be the subject-level ICC,

if there’s no random slopes, i.e. correlation between time points for the same subject.`study_parameters()`

: 0 and NA now means different things. If 0 is passed, the parameters

is kept in the model, if you want to remove it specify it as NA instead.`study_parameters()`

: is now less flexible, but more robust. Previously a large

combination if raw and relative parameters could be combined, and the individual

parameters was solved for. To make the function less bug prone and easier to maintain,

it is now only possible to specify the cluster-level variance components as relative values,

if the other parameters as passed as raw inputs.

## Bug fixes and minor changes

- Output from
`simulate_data()`

now includes a column`y_c`

that contains the full outcome vector,

without missing values added. This makes it easy to compare the complete and incomplete

data set, e.g. via`simulate()`

. `simulate()`

new argument`batch_progress`

enables showing progress when doing

multiple simulations.- Fix bug in
`summary.plcp_sim`

where the wrong % convergence was calculated. - Simulation function now accepts lme4 formulas containing “||”.
- The cluster-level intercept variance is now also set to zero in the control

group, when a partially nested design is requested. - Fix incorrect error message from
`study_parameters`

when

`icc_cluster_pre = NULL`

and all inputs are standardized. - Fix bug that would cause all slopes to be zero when
`var_ratio`

argument was

passed a vector of values including a 0, e.g.`var_ratio = c(0, 0.1, 0.2)`

. - Fix bug for multi-sim objects that caused the wrong class the be used for,

e.g.`res[[1]]$paras`

, and thus the single simulation would not print

correctly. - Results from multi-sim objects can now be summarized for all random effects

in the model. - More support for summarizing random effects from partially nested formulas,

e.g.`cluster_intercept`

and`cluster_slope`

is now correctly extracted from

`(0 + treatment + treatment:time || cluster)`

. - When Satterthwaite’s method fails the between clusters/subjects DFs

are used to calculate*p*-values. `Power.plcp_multi`

is now exported.`get_power.plcp_multi`

now shows a progress bar.- Fix a bug that caused dropout to be wrong when one condition had 0 dropout, and

`deterministic_dropout = FALSE`

.

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