This is a bug fix release

- Support
`ggplot >= 3.4.0`

,`tidyselect >= 1.2.0`

, and newer`future`

by replacing deprecated functions. - Accept
`mcp(..., cores = "all")`

. - Fix documentation of
`iter`

argument to`mcp()`

. - Other small fixes to deployment and documentation.

This release contains no user-facing changes. The test suite suite is now compatible with dplyr 1.0.8, which caused the test suite to fail. This, in turn, would trigger the removal of mcp from CRAN.

This is mostly a bug fix release.

`ex = mcp_example("demo", with_fit = TRUE)`

is the new interface that replaces the`ex_*`

datasets in prior versions. This reduces clutter of the namespace/documentation and the size of the package. It also gives the user richer details on the simulation and analyses. For “demo”, the`ex_demo`

dataset is now`ex$data`

and the`ex_fit`

is`ex$fit`

.Nicer printing of lists and texts all over. E.g., try

`print(demo_fit$jags_code)`

and`print(demo_fit$pars)`

.

- Support breaking changes in
`tidybayes >= 3.0.0`

and`dplyr >= 1.0.6`

Get fits and predictions for in-sample and out-of-sample data. Read more in the article on these functions.

- Use
`predict(fit)`

to get predicted values and quantiles. - Use
`fitted(fit)`

to get estimated values and quantiles. - Use
`residuals(fit)`

to get residuals and quantiles.

All of the above functions include many arguments that align with (and extends) the options already in

`plot.mcpfit()`

, including getting fits/predictions for sigma (`which_y = "sigma"`

), for the prior (`prior = TRUE`

), and arbitrary quantiles (`probs = c(0.1, 0.5, 0.999)`

). Use the`newdata`

argument to get out-of-sample fitted/predicted values. Set`summary = FALSE`

to get per-draw values.- Use
Added support for weighted regression for gaussian families:

`model = list(y | weights(weight_column) ~ 1 + x)`

. Weights are visualized as dot sizes in`plot(fit)`

.Support for more link functions across families (e.g.,

`family = gaussian(link = "log")`

):`gaussian`

: “identity”, “log”`binomial`

: “logit”, “probit”, “identity”`bernoulli`

: “logit”, “probit”, “identity”`poisson`

: “log”, “identity”

New argument

`scale`

in`fitted()`

,`plot()`

, and`fit$simulate()`

. When`scale = "response"`

(default), they return fits on the observed scale. When`scale = "linear"`

, they return fits on the parameter scale where the linear trends are. Useful for model understanding and debugging.Use

`pp_check(fit)`

to do prior/posterior predictive checking. See`pp_check(fit, type = "x")`

for a list of plot types.`pp_check(fit, facet_by = "varying_column")`

facets by a data column.Improvements to

`plot()`

:- Change point densities are now computed on a per-panel basis in
`plot(fit, facet_by = "varying_column")`

. Previous releases only displayed population-level change points. - You can now plot varying effects with
`rate = FALSE`

for binomial models. - Change point densities in
`plot(fit)`

are not located directly on the x-axis. They were “floating” 5% above the x-axis in the previous releases.

- Change point densities are now computed on a per-panel basis in
New argument

`nsamples`

reduces the number of samples used in most functions to speed up processing.`nsamples = NULL`

uses all samples for maximum accuracy.New argument

`arma`

in many functions toggles whether autoregressive effects should be modelled.Although the API is still in alpha, feel free to try extracting samples using

`mcp:::tidy_samples(fit)`

. This is useful for further processing using`tidybayes`

,`bayesplot`

, etc. and is used extensively internally in`mcp`

. One useful feature is computing absolute values for varying change points:`mcp:::tidy_samples(fit, population = FALSE, absolute = TRUE)`

. Feedback is appreciated before`tidy_samples`

will to become part of the`mcp`

API in a future release.

- Change point densities in
`plot(fit)`

are now scaled to 20% of the plot for each chain X changepoint combo. This addresses a common problem where a wide posterior was almost invisibly low when a narrow posterior was present. This means that heights should only be compared*within*each chain x changepoint combo - not across. - Removed the implicit ceiling of 1000 lines and samples in
`plot.mcpfit()`

. - Rownames are removed from
`ranef()`

and`fixef()`

returns. - A major effort has been put into making
`mcp`

robust and agile to develop.`mcp`

now use defensive programming with helpful error messages. The Test suite includes 3600+ tests. `plot()`

,`predict()`

, etc. are now considerably faster for AR(N) due to vectorization of the underlying code.

- Sigma is now forced to stay positive via a floor at 0.
- Fixed: support and require dplyr 1.0.0. Now also requires tidybayes 2.0.3.
- Fixed: Parallel sampling sometimes produced identical chains.
- Fixed several small bugs

The API and internal structure should be stable now. v0.2.0 will be released on CRAN.

- Model quadratic and other terms using
`I(x^2)`

,`I(x^3.24)`

,`sin(x)`

,`sqrt(x)`

, etc. - Model variance for
`family = gaussian()`

using`~ sigma([formula here])`

. - Model Nth order autoregressive models using
`~ ar(order, formula)`

, typically like`y ~ 1 + x + ar(2)`

for AR(2). Simulate AR(N) models from scratch or given known data with`fit$simulate()`

. The article on AR(N) has more details and examples. AR(N) models are popular to detect changes in time-series. - Many updates to
`plot()`

.- Includes the posterior densities of the change point(s). Disable
using
`plot(fit, cp_dens = FALSE)`

. - Supports AR(N) models (see above).
- Plot posterior parameter intervals using
`plot(fit, q_fit = TRUE)`

.`plot(fit, q_fit = c(0.025, 0.5, 0.975))`

plots 95% HDI and the median. - Plot prediction intervals using
`plot(fit, q_predict = TRUE)`

. - Choose data geom. Currently takes “point” (default) and “line”
(
`plot(fit, geom_data = "line")`

). The latter is useful for time series. Disable using`geom_data = FALSE`

.

- Includes the posterior densities of the change point(s). Disable
using
- Use
`options(mc.cores = 3)`

for considerable speed gains for the rest of the session. All vignettes/articles have been updated to recommend this as a default, though serial sampling is still the technical default.`mcp(..., cores = 3)`

does the same thing on a call-by-ball basis. `fit$simulate()`

adds the simulation parameters as an attribute (`attr(y, "simulate")`

) to the predicted variable.`summary()`

recognizes this and adds the simulated values to the results table (columns`sim`

and`match`

) so that one can inspect whether the values were recovered.- Use
`plot(fit, which_y = "sigma")`

to plot the residual standard deviation on the y-axis. It works for AR(N) as well, e.g.,`which_y = "ar1"`

,`which_y = "ar2"`

, etc. This is useful to visualize change points in variance and autocorrelation. The vignettes on variance and autocorrelations have been updated with worked examples. - Much love for the priors:
- Set a Dirichlet prior on the change points using
`prior = list(cp_1 = "dirichlet(1)", cp_2 = ...)`

. Read pros and cons here. - The default prior has been changed from “truncated-uniforms” to a “t-tail” prior to be more uninformative while still sampling effectively. Read more here.
- You can now sample the prior using
`mcp(..., sample = "prior")`

or`mcp(..., sample = "both")`

and most methods can now take the prior:`plot(fit, prior = TRUE)`

,`plot_pars(fit, prior = TRUE)`

,`summary(fit, prior = TRUE)`

,`ranef(fit, prior = TRUE)`

.

- Set a Dirichlet prior on the change points using
`mcp`

can now be cited! Call`citation("mcp")`

or see the pre-print here: https://osf.io/fzqxv.

- Some renaming: “segments” –> “model”.
`fit$func_y()`

–>`fit$simulate()`

. `plot()`

only visualize the total fit while`plot_pars()`

only visualize individual parameters. These functions were mixed in`plot()`

previously.- The argument
`update`

has been discarded from`mcp()`

(it’s all on`adapt`

now) and`inits`

has been added. - Many internal changes to make
`mcp`

more future proof. The biggest internal change is that`rjags`

and`future`

replace the`dclone`

package. Among other things, this gives faster and cleaner installations. - Many more informative error messages to help you quickly understand and solve errors.
- Updated documentation and website.

First public release.

- Varying change points
- Basic GLM: Gaussian, binomial, Bernoulli, and Poisson, and associated vignettes.
- summary(fit), fixef(fit), and ranef(fit)
- plot(fit, “segments”) and plot(fit, “bayesplot-name-here”) with some options
- 1000+ basic unit tests to ensure non-breaking code for a wide variety of models.
- Testing and model comparison using
`loo`

and`hypothesis`