# cnorm: Central Probability in a Normal or T Distribution In ProjectMOSAIC/mosaic: Project MOSAIC Statistics and Mathematics Teaching Utilities

## Description

These versions of the quantile functions take a vector of central probabilities as its first argument.

## Usage

 ```1 2 3 4``` ```cnorm(p, mean = 0, sd = 1, log.p = FALSE, side = c("both", "upper", "lower")) ct(p, df, ncp, log.p = FALSE, side = c("upper", "lower", "both")) ```

## Arguments

 `p` vector of probabilities. `mean` vector of means. `sd` vector of standard deviations. `log.p` logical; if TRUE, probabilities p are given as log(p). `side` One of "upper", "lower", or "both" indicating whether a vector of upper or lower quantiles or a matrix of both should be returned. `df` degrees of freedom (> 0, maybe non-integer). ```df = Inf``` is allowed. `ncp` non-centrality parameter delta; currently except for `rt()`, only for `abs(ncp) <= 37.62`. If omitted, use the central t distribution.

`stats::qnorm()`, `cdist()`
 ```1 2 3 4 5 6 7 8 9``` ```qnorm(.975) cnorm(.95) xcnorm(.95) xcnorm(.95, verbose = FALSE, return = "plot") %>% gf_refine( scale_fill_manual( values = c("navy", "limegreen")), scale_color_manual(values = c("black", "black"))) cnorm(.95, mean = 100, sd = 10) xcnorm(.95, mean = 100, sd = 10) ```