akishiroshita.r-universe.dev

[akishiroshita] foresty 0.2.0.9000

akihirokun8@gmail.com (Akihiro Shiroshita) — Sat, 26 Sep 2026 02:16:49 GMT

Draws forest plots of exposure effects from fitted regression models. Name an exposure and 'foresty' plots its effect. Name an effect modifier as well and it refits the model with the interaction term, estimates the exposure effect within each level of the modifier as a linear combination of the coefficients, and reports the joint interaction test beside those estimates. It takes one exposure and one modifier at a time, so the interaction is always a two-way one. Rows of the plot and of the table beside it share one scale, in a layout that can follow a journal's house style. The same results go to a self-contained HTML page holding the subgroup estimates, the joint test and the coefficient table. The 'car' package computes the linear combinations and their tests. Models fitted by stats::glm(), stats::lm(), the 'survival' package, the 'lme4' package, the 'geepack' package and the 'survey' package are supported, as is any fit supplying coef() and vcov(). Ordinal outcomes are supported through the 'MASS' package and nominal ones through the 'nnet' package, where the figure carries one row per level of the outcome and the interaction is tested jointly across the equations. Fits from the 'rms' package are refused, naming the function that fits the same model in their place. The estimation of an exposure effect within a level of a modifier, and the test of the difference between such estimates, follow Altman and Bland (2003) and VanderWeele and Knol (2014) ; the reporting of subgroup effects beside the interaction test follows Wang et al. (2007) , and the figure itself the forest plot described by Lewis and Clarke (2001) .

[akishiroshita] dtlog 0.2.0.9000

akihirokun8@gmail.com (Akihiro Shiroshita) — Sat, 26 Sep 2026 02:16:45 GMT

Provides feedback about 'data.table' operations. 'dtlog' redefines the subsetting method for data tables as well as several functions exported by 'data.table' so that each operation prints a short message describing what it did: how many rows were removed, which columns were added, updated or dropped, how many groups an aggregation produced, and so on. The operations themselves are left untouched, including modification by reference. It also provides dttable(), which describes the variables a single data table holds and passes every other call on to base::table() unchanged. Inspired by the 'tidylog' package.

[akishiroshita] ggstratify 0.2.0.9000

akihirokun8@gmail.com (Akihiro Shiroshita) — Fri, 25 Sep 2026 15:56:43 GMT

A point-and-click 'shiny' interface for the descriptive analysis that comes before any model is chosen. Pass a data frame, pick the variable to describe, and add the layers you want to see it within: a second variable becomes the panels of a 'ggplot2' facet_wrap(), and further variables become separate figures, one file each, taken either one variable at a time or crossed. Every stratum is reported with the number of observations behind it, on the figure and on each of its panels; strata that contain none are listed rather than dropped, and rows with a missing value in a layer variable are excluded and counted. A continuous variable can be categorized into quantile groups, equal-width bins or user-supplied cut points, a variable of any type can be turned into whether it is missing or observed, so that the rows a layer would exclude become a stratum of their own, and a date or date-time variable can be read at a chosen resolution, either as a calendar period or as a position in the yearly cycle such as the month or the season; any of them can then be used as a layer. The figure types follow those offered by the 'ggplotgui' package and add the line plot for change over time, an optional LOWESS smoother, and the Kaplan-Meier curve estimated by 'survival', with an optional number-at-risk table. An error bar can show a standard error or a confidence interval, the latter from the t distribution for a mean and from the Clopper-Pearson or Wilson method for a proportion, and the points can be joined by a line computed from the same summary, which is how a trend over time is read. A survey weight can be set: the figure is then drawn from the weighted data, every count is reported both as rows and as the sum of the weights, and error bars and confidence bands are design-based estimates from the 'survey' package. Columns are described as they are typed, so convert each to the type you mean first. Figures are written as PNG or SVG, and the application prints the 'ggplot2' code behind the figure on screen, so that a description can be repeated, shared or accounted for later. Everything runs locally, with no network access and no AI involved.