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. - Set a Dirichlet prior on the change points using
`prior = list(cp_1 = "dirichlet(1)", cp_2 = ...)`

. Read pros and cons here. `mcp`

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

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

- 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
- 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`