docs: clarify that p* functions are row-wise, not OpenMP-parallel

README.md

@@ -23,16 +23,18 @@ install.packages("kit", repos = "https://fastverse.r-universe.dev")
 
 ## Features
 
-### Parallel Statistical Functions
+### Row-wise Statistical Functions
 
-Vector-valued functions operating in parallel over vectors or data frames:
+Vector-valued functions that combine several vectors or data frame columns element by element:
 
-- **`psum`, `pprod`, `pmean`**: Parallel sum, product, and mean
-- **`fpmin`, `fpmax`, `prange`**: (Fast) parallel minimum, maximum, and range
-- **`pall`, `pany`**: Parallel all/any operations
+- **`psum`, `pprod`, `pmean`**: Row-wise sum, product, and mean
+- **`fpmin`, `fpmax`, `prange`**: Row-wise minimum, maximum, and range
+- **`pall`, `pany`**: Row-wise all/any operations
 - **`pcount`, `pcountNA`**: Count occurrences of values or NAs
 - **`pfirst`, `plast`**: First/last non-missing values
 
+The word "parallel" in their names means they work across several vectors at once, not that they use multiple threads. These functions are single-threaded C code. Only `iif()`, `nswitch()`, `vswitch()`, `psort()`, and `charToFact()` can use OpenMP, and only when you raise `options(kit.nThread)` above its default of 1.
+
 ```r
 x <- c(1, 3, NA, 5)
 y <- c(2, NA, 4, 1)

_pkgdown.yml

@@ -44,8 +44,8 @@ navbar:
 
 
 reference:
-- title: "Parallel Statistical Functions"
-  desc: "Vector-valued (statistical) functions operating in parallel over vectors passed as arguments, or a single list of vectors/data frame."
+- title: "Row-wise Statistical Functions"
+  desc: "Vector-valued (statistical) functions that combine several vectors passed as arguments, or a single list of vectors/data frame, element by element."
 - contents:
   - parallel-funs
 - title: "Vectorised and Nested Switches"

man/psum.Rd

@@ -18,6 +18,8 @@
 \title{Parallel (Statistical) Functions}
 \description{
 Vector-valued (statistical) functions operating in parallel over vectors passed as arguments, or a single list of vectors (such as a data frame). Similar to \code{\link{pmin}} and \code{\link{pmax}}, except that these functions do not recycle vectors.
+
+The word "parallel" here means that the functions work across several vectors at once. It does not mean multi-threading: these functions are single-threaded C code.
 }
 \usage{
 psum(..., na.rm = FALSE)

vignettes/introduction.Rmd

@@ -24,31 +24,31 @@ library(kit)
 
 Key features include:
 
-*   **Parallel statistical functions**: Row-wise operations (`psum`, `pmean`, `pfirst`) using OpenMP.
+*   **Row-wise statistical functions**: Operations across several vectors at once (`psum`, `pmean`, `pfirst`). These functions are single-threaded C code.
 *   **Vectorized conditionals**: Fast `if-else` logic (`iif`, `nif`, `vswitch`) that preserves attributes.
 *   **Efficient set operations**: Faster `unique`, `duplicated`, and `count` for vectors and data frames.
 *   **Partial sorting**: Retrieve top N elements without sorting the entire vector (`topn`).
 *   **Factor utilities**: Fast character-to-factor conversion (`charToFact`) and level manipulation (`setlevels`).
 
-Most functions are implemented in C and support multi-threading where applicable, making them significantly faster than their base R equivalents on large datasets.
+Most functions are implemented in C, making them significantly faster than their base R equivalents on large datasets. Five functions can use OpenMP when you raise `options(kit.nThread)` above its default of 1: `iif()`, `nswitch()`, `vswitch()`, `psort()`, and `charToFact()`. The other functions, including the `p*` functions, run on one thread.
 
-## Parallel Statistical Functions
+## Row-wise Statistical Functions
 
 Computing row-wise statistics across multiple vectors or data frame columns is a common task. While base R has `pmin()` and `pmax()`, it lacks efficient equivalents for sum, mean, or product. **kit** fills this gap.
 
 ### Row-wise Arithmetic
 
-`psum()`, `pmean()`, and `pprod()` compute parallel sum, mean, and product respectively. They accept multiple vectors or a single list/data frame.
+`psum()`, `pmean()`, and `pprod()` compute row-wise sum, mean, and product respectively. They accept multiple vectors or a single list/data frame.
 
 ```{r}
 x <- c(1, 3, NA, 5)
 y <- c(2, NA, 4, 1)
 z <- c(3, 4, 4, 1)
 
-# Parallel sum
+# Row-wise sum
 psum(x, y, z, na.rm = TRUE)
 
-# Parallel mean
+# Row-wise mean
 pmean(x, y, z, na.rm = TRUE)
 ```
 
@@ -61,20 +61,20 @@ psum(df)
 
 ### Row-wise Min, Max, and Range
 
-`fpmin()`, `fpmax()`, and `prange()` compute parallel minimum, maximum, and range (max - min) respectively. They complement base R's `pmin()` and `pmax()`, providing greater performance and the ability to work efficiently with data frames.
+`fpmin()`, `fpmax()`, and `prange()` compute row-wise minimum, maximum, and range (max - min) respectively. They complement base R's `pmin()` and `pmax()`, providing greater performance and the ability to work efficiently with data frames.
 
 ```{r}
 x <- c(1, 3, NA, 5)
 y <- c(2, NA, 4, 1)
 z <- c(3, 4, 4, 1)
 
-# Parallel minimum
+# Row-wise minimum
 fpmin(x, y, z, na.rm = TRUE)
 
-# Parallel maximum
+# Row-wise maximum
 fpmax(x, y, z, na.rm = TRUE)
 
-# Parallel range (max - min)
+# Row-wise range (max - min)
 prange(x, y, z, na.rm = TRUE)
 ```