update cloud

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: SummaryTables
 Title: Publication-Ready Summary Tables for Jamovi
-Version: 1.3.0
+Version: 1.3.1
 Authors@R: 
     person("Nour Edin", "Darwish", , "nouredindarwish@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0009-0009-5527-4539"))

NEWS.md

@@ -1,3 +1,7 @@
+## SummaryTables 1.3.1
+
+* Improved the message shown when required variables are missing.
+
 ## SummaryTables 1.3.0
 
 * Added Standardized Coefficients for Linear Regression analyses (both Univariable and Multivariable).

R/tblcontinuous.b.R

@@ -8,7 +8,7 @@ tblContinuousClass <- R6::R6Class(
 
       if (is.null(contVar) || length(varsCat) == 0) {
         renderPlaceholder(
-          "Add a continuous variable and at least one categorical variable to generate the table", # nolint
+          "Add a Continuous Variable and at least one Categorical Variable to generate the table", # nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblcross.b.R

@@ -7,7 +7,7 @@ tblCrossClass <- R6::R6Class(
       col <- self$options$col
       if (is.null(row) || is.null(col)) {
         renderPlaceholder(
-          "Add a row variable and a column variable to generate the table",
+          "Add a Row Variable and a Column Variable to generate the table",
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tbllikert.b.R

@@ -6,7 +6,7 @@ tblLikertClass <- R6::R6Class(
       vars <- self$options$vars
       if (length(vars) == 0) {
         renderPlaceholder(
-          "Add Likert scale variables to generate the table",
+          "Add at least one Likert-scale Variable to generate the table",
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblregcox.b.R

@@ -9,7 +9,7 @@ tblRegCoxClass <- R6::R6Class(
 
       if (is.null(elapsed) || is.null(event) || length(terms) == 0) {
         renderPlaceholder(
-          "Add a time variable, an event variable, and at least one term to generate the table", #nolint
+          "Add a Time variable, an Event variable, and at least one Model Term to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblreglinear.b.R

@@ -8,7 +8,7 @@ tblRegLinearClass <- R6::R6Class(
 
       if (is.null(dep) || length(terms) == 0) {
         renderPlaceholder(
-          "Add a dependent variable and at least one term to generate the table", #nolint
+          "Add a Dependent Variable and at least one Model Term to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblreglogistic.b.R

@@ -8,7 +8,7 @@ tblRegLogisticClass <- R6::R6Class(
 
       if (is.null(dep) || length(terms) == 0) {
         renderPlaceholder(
-          "Add a dependent variable and at least one term to generate the table", #nolint
+          "Add a Dependent Variable and at least one Model Term to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblsummary.b.R

@@ -10,7 +10,7 @@ tblSummaryClass <- R6::R6Class(
 
       if (!hasCont && !hasCat) {
         renderPlaceholder(
-          "Add continuous or categorical variables to generate the table",
+          "Add at least one Continuous or Categorical Variable to generate the table",
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblsurvfit.b.R

@@ -8,7 +8,7 @@ tblSurvfitClass <- R6::R6Class(
 
       if (is.null(elapsed) || is.null(event)) {
         renderPlaceholder(
-          "Add a time variable and an event variable to generate the table",
+          "Add a Time variable and an Event variable to generate the table",
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tbluniregcox.b.R

@@ -14,7 +14,7 @@ tblUniRegCoxClass <- R6::R6Class(
           (length(covs) == 0 && length(factors) == 0)
       ) {
         renderPlaceholder(
-          "Add a time variable, an event variable, and at least one covariate or factor to generate the table", #nolint
+          "Add a Time variable, an Event variable, and at least one Covariate or Factor to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblunireglinear.b.R

@@ -9,7 +9,7 @@ tblUniRegLinearClass <- R6::R6Class(
 
       if (is.null(dep) || (length(covs) == 0 && length(factors) == 0)) {
         renderPlaceholder(
-          "Add a dependent variable and at least one covariate or factor to generate the table", #nolint
+          "Add a Dependent Variable and at least one Covariate or Factor to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

R/tblunireglogistic.b.R

@@ -9,7 +9,7 @@ tblUniRegLogisticClass <- R6::R6Class(
 
       if (is.null(dep) || (length(covs) == 0 && length(factors) == 0)) {
         renderPlaceholder(
-          "Add a dependent variable and at least one covariate or factor to generate the table", #nolint
+          "Add a Dependent Variable and at least one Covariate or Factor to generate the table", #nolint
           self$results$tbl
         )
         self$results$status$setVisible(FALSE)

docs/user-guide.md

@@ -11,8 +11,8 @@ This guide highlights important notes, default behaviors, and specific options y
 
 A quick reference guide for choosing the right table based on your data and goals:
 
-* **Table 1 / Main Summary:** Use the **Summary Table** without a grouping variable for a general overview.
-* **Categorical Outcome:** Use the **Summary Table** with your outcome assigned to the **Grouping Variable**.
+* **Table 1 / Main Summary:** Use the **Summary Table** without adding a **Grouping Variable** for a general overview.
+* **Categorical Outcome:** Use the **Summary Table** with your outcome added to the **Grouping Variable**.
 * **Continuous Outcome:** Use the **Continuous Table**.
 * **Only Two Categorical Variables:** Use the **Cross Table** for a straightforward cross-tabulation.
 * **Likert Scale Data:** Use the **Likert Table**.
@@ -38,7 +38,7 @@ To prevent this snowballing delay, you can enable *Manual Run Mode*.
   ![Screenshot showing the Run manually option](assets/run-manually.png){ loading=lazy width="500" }
 </figure>
 
-Checking the **"Run manually"** option disables the auto-run behavior and activates the **"Run"** button. This allows you to add all 10 variables at once and set all your options without triggering any calculations. Once everything is set up, click **"Run"** to calculate the final table exactly once—fitting just the *10 models* you actually need. This saves a huge amount of time, especially for computationally heavy tables like regressions.
+Checking the **Run manually** option disables the auto-run behavior and activates the **Run** button. This allows you to add all 10 variables at once and set all your options without triggering any calculations. Once everything is set up, click **Run** to calculate the final table exactly once—fitting just the *10 models* you actually need. This saves a huge amount of time, especially for computationally heavy tables like regressions.
 
 ### Save to Word
 
@@ -71,20 +71,20 @@ You can independently set the rounding rules for various elements in your tables
 
 #### P-Values
 
-The p-value dropdown controls the rounding of *large* p-values, while precision automatically increases as p-values get smaller.
+The p-value **Decimal places** dropdown controls the rounding of *large* p-values, while precision automatically increases as p-values get smaller.
 
 <figure markdown="span">
   ![The large p-value decimal places selection dropdown showing Auto and numerical precision options](assets/pvalue-digits.png){ loading=lazy width="500" }
 </figure>
 
 * **Auto (Default):** Depends on the theme (the default theme uses **"1"**).
-* **1:** Large p-values are rounded to one decimal place. Precision increases as p-values decrease, and very small values are shown as `<0.001`.
-* **2:** Large p-values are rounded to two decimal places. Precision increases as p-values decrease, and very small values are shown as `<0.001`.
-* **3:** Large p-values are rounded to three decimal places. Precision increases as p-values decrease, and very small values are shown as `<0.001`.
+* **1:** Large p-values are rounded to 1 decimal place. Precision automatically increases to 2, then 3 decimal places as values get smaller. Extremes are shown as `>0.9` and `<0.001`.
+* **2:** Large p-values are rounded to 2 decimal places. Precision automatically increases to 3 decimal places as values get smaller. Extremes are shown as `>0.99` and `<0.001`.
+* **3:** All p-values are rounded to 3 decimal places. Extremes are shown as `>0.999` and `<0.001`.
 
 ### Statistical Tests
 
-The module automatically selects appropriate statistical tests based on your data types and the number of groups across the **Summary Table**, **Continuous Table**, and **Cross Table**. You can configure this behavior in the **Default test** dropdowns:
+The module automatically selects appropriate statistical tests based on your data types and the number of groups across the **Summary Table**, **Continuous Table**, and **Cross Table**. You can configure this behavior using the **Default test** dropdowns:
 
 #### Continuous Variables
 
@@ -96,7 +96,7 @@ The module automatically selects appropriate statistical tests based on your dat
 * **Non-parametric:** Uses the Wilcoxon rank-sum test for 2 groups, or Kruskal-Wallis rank-sum test for >2 groups.
 
 !!! info "Grouping Variable in the Continuous Table"
-    If you assign a **Grouping Variable** in the **Continuous Table**, the module automatically calculates p-values using a two-way ANOVA. In this specific configuration, no other statistical tests can be applied.
+    If you add a **Grouping Variable** in the **Continuous Table**, the module automatically calculates p-values using a two-way ANOVA. In this specific configuration, no other statistical tests can be applied.
 
 #### Categorical Variables
 
@@ -121,25 +121,25 @@ If you want specific tests for specific variables, you can manually select a dif
 ### Summary Table: Difference
 
 !!! info "SMD Method Calculation"
-    When you select the **SMD** option as your **Difference** method, the values are calculated using the [`smd` R package](https://bsaul.github.io/smd/index.html).
+    When you select **SMD** as your **Difference** method, the values are calculated using the [`smd` R package](https://bsaul.github.io/smd/index.html).
 
     <figure markdown="span">
       ![jamovi interface showing the SMD method selected under the Difference options](assets/difference-smd.png){ loading=lazy width="500" }
     </figure>
 
 !!! failure "Multiple P-Value Columns Error"
-    If you select a **Difference** method that generates a p-value and you also check **P-value** under the general **P-value** section, an error will occur. The table cannot display multiple p-value columns simultaneously.
+    If you select a **Difference** method that generates a p-value and you also check the **P-value** option under the general **P-value** section, an error will occur. The table cannot display multiple p-value columns simultaneously.
 
     <figure markdown="span">
       ![Error message displayed in jamovi when attempting to add multiple p-value columns](assets/multiple-pvalues-error.png){ loading=lazy width="500" }
     </figure>
 
 ### Regression Tables: Univariable vs. Multivariable
 
-It is important to understand the fundamental difference in how the **Univariable Regression** and **Multivariable Regression** tables are constructed:
+It is important to understand the fundamental difference in how **Univariable Regression** and **Multivariable Regression** tables are constructed:
 
-* **Univariable Regression:** Fits *one separate model per predictor*. If you add 5 variables across the **Covariates** and **Factors** lists, the module will fit 5 distinct simple regression models (each predicting the dependent variable using just that one predictor) and combine the results into a single table.
-* **Multivariable Regression:** Fits *one single model containing all predictors*. If you add 5 variables across the **Covariates** and **Factors** lists, the module will fit a single model where all 5 variables are included simultaneously, adjusting for each other.
+* **Univariable Regression:** Fits *one separate model per predictor*. If you add 5 variables to **Covariates** and **Factors**, the module will fit 5 distinct simple regression models (each predicting the dependent variable using just that one predictor) and combine the results into a single table.
+* **Multivariable Regression:** Fits *one single model containing all predictors*. If you add 5 variables to **Covariates** and **Factors**, the module will fit a single model where all 5 variables are included simultaneously, adjusting for each other.
 
 ### Regression Tables: Standardized Coefficients
 
@@ -154,7 +154,7 @@ For linear regression models, SummaryTables allows you to report standardized co
 
 ### Survival and Cox Regression: Event Variable Coding
 
-When using the **Survival Table** or **Cox Regression** analyses, the **Event variable** must be coded correctly:
+When using the **Survival Table** or **Cox Regression** analyses, the **Event** variable must be coded correctly:
 
 <figure markdown="span">
   ![jamovi interface showing the Event variable assignment and Event level selection](assets/event-variable-options.png){ loading=lazy width="500" }

jamovi/0000.yaml

@@ -1,12 +1,12 @@
 ---
 title: Publication-Ready Summary Tables
 name: SummaryTables
-version: 1.3.0
+version: 1.3.1
 jms: '1.0'
 authors:
   - Nour Edin Darwish
 maintainer: Nour Edin Darwish <nouredindarwish@gmail.com>
-date: '2026-05-23'
+date: '2026-05-24'
 type: R
 description: >-
   A comprehensive module for creating publication-ready summary and regression