tool-docs.md
In btw: A Toolkit for Connecting R and Large Language Models

btw_tool_docs_help_page() works

Code
  cli::cat_line(res@value)
Output
  ## `help(package = "stats", "rnorm")`

  ### The Normal Distribution

  #### Description

  Density, distribution function, quantile function and random generation
  for the normal distribution with mean equal to `mean` and standard
  deviation equal to `sd`.

  #### Usage

  ``` R
  dnorm(x, mean = 0, sd = 1, log = FALSE)
  pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
  qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)
  rnorm(n, mean = 0, sd = 1)
  ```

  #### Arguments

  |  |  |
  |----|----|
  | `x`, `q` | vector of quantiles. |
  | `p` | vector of probabilities. |
  | `n` | number of observations. If `length(n) > 1`, the length is taken to be the number required. |
  | `mean` | vector of means. |
  | `sd` | vector of standard deviations. |
  | `log`, `log.p` | logical; if TRUE, probabilities p are given as log(p). |
  | `lower.tail` | logical; if TRUE (default), probabilities are `P[X \le x]` otherwise, `P[X > x]`. |

  #### Details

  If `mean` or `sd` are not specified they assume the default values of
  `0` and `1`, respectively.

  The normal distribution has density

  ` f(x) = \frac{1}{\sqrt{2\pi}\sigma} e^{-(x-\mu)^2/2\sigma^2}`

  where `\mu` is the mean of the distribution and `\sigma` the standard
  deviation.

  #### Value

  `dnorm` gives the density, `pnorm` gives the distribution function,
  `qnorm` gives the quantile function, and `rnorm` generates random
  deviates.

  The length of the result is determined by `n` for `rnorm`, and is the
  maximum of the lengths of the numerical arguments for the other
  functions.

  The numerical arguments other than `n` are recycled to the length of the
  result. Only the first elements of the logical arguments are used.

  For `sd = 0` this gives the limit as `sd` decreases to 0, a point mass
  at `mu`. `sd < 0` is an error and returns `NaN`.

  #### Source

  For `pnorm`, based on

  Cody, W. D. (1993) Algorithm 715: SPECFUN – A portable FORTRAN package
  of special function routines and test drivers. *ACM Transactions on
  Mathematical Software* **19**, 22–32.

  For `qnorm`, the code is based on a C translation of

  Wichura, M. J. (1988) Algorithm AS 241: The percentage points of the
  normal distribution. *Applied Statistics*, **37**, 477–484;
  [doi:10.2307/2347330](https://doi.org/10.2307/2347330).

  which provides precise results up to about 16 digits for `log.p=FALSE`.
  For log scale probabilities in the extreme tails, since
  <span class="rlang">**R**</span> version 4.1.0, extensively since 4.3.0,
  asymptotic expansions are used which have been derived and explored in

  Maechler, M. (2022) Asymptotic tail formulas for gaussian quantiles;
  [<span class="pkg">DPQ</span>](https://CRAN.R-project.org/package=DPQ)
  vignette
  <https://CRAN.R-project.org/package=DPQ/vignettes/qnorm-asymp.pdf>.

  For `rnorm`, see RNG for how to select the algorithm and for references
  to the supplied methods.

  #### References

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) *The New S
  Language*. Wadsworth & Brooks/Cole.

  Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) *Continuous
  Univariate Distributions*, volume 1, chapter 13. Wiley, New York.

  #### See Also

  Distributions for other standard distributions, including `dlnorm` for
  the *Log*normal distribution.

  #### Examples

  ``` R
  require(graphics)

  dnorm(0) == 1/sqrt(2*pi)
  dnorm(1) == exp(-1/2)/sqrt(2*pi)
  dnorm(1) == 1/sqrt(2*pi*exp(1))

  ## Using "log = TRUE" for an extended range :
  par(mfrow = c(2,1))
  plot(function(x) dnorm(x, log = TRUE), -60, 50,
       main = "log { Normal density }")
  curve(log(dnorm(x)), add = TRUE, col = "red", lwd = 2)
  mtext("dnorm(x, log=TRUE)", adj = 0)
  mtext("log(dnorm(x))", col = "red", adj = 1)

  plot(function(x) pnorm(x, log.p = TRUE), -50, 10,
       main = "log { Normal Cumulative }")
  curve(log(pnorm(x)), add = TRUE, col = "red", lwd = 2)
  mtext("pnorm(x, log=TRUE)", adj = 0)
  mtext("log(pnorm(x))", col = "red", adj = 1)

  ## if you want the so-called 'error function'
  erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
  ## (see Abramowitz and Stegun 29.2.29)
  ## and the so-called 'complementary error function'
  erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE)
  ## and the inverses
  erfinv <- function (x) qnorm((1 + x)/2)/sqrt(2)
  erfcinv <- function (x) qnorm(x/2, lower = FALSE)/sqrt(2)
  ```

btw_tool_docs_help_page() with unknown topic

Code
  btw_tool_docs_help_page("unknown-topic", "dplyr")
Condition
  Error in `btw_tool_docs_help_page()`:
  ! No help page found for topic "unknown-topic" in package dplyr.
  i To search in all packages, call `btw_tool_docs_help_page()` with an empty string for `package_name`.
Code
  btw_tool_docs_help_page("unknown-topic")
Condition
  Error in `btw_tool_docs_help_page()`:
  ! No help page found for topic "unknown-topic" in all installed packages.

btw_tool_docs_help_page() with multiple help topics

Code
  btw_tool_docs_help_page("filter")
Condition
  Error in `btw_tool_docs_help_page()`:
  ! Topic "filter" matched 2 different topics.
  i Choose one or submit individual tool calls for each topic.
  * {"topic":"filter", "package_name":"dplyr"}
  * {"topic":"filter", "package_name":"stats"}