The Cprob package permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function).

You can download the stable version on CRAN

```
install.packages("Cprob")
```

Or you can install the development version from github

```
## if necessary
## install.packages("devtools")
devtools::install_github("aallignol/Cprob")
```

The conditional probability function can be estimated using the `cpf`

function.

```
library(Cprob)
mgus$AGE <- ifelse(mgus$age < 64, 0, 1)
CP <- cpf(Hist(time, ev)~AGE, data = mgus)
CP
summary(CP)
```

A regression model can be fitted either using temporal process regression

```
fit.cpfpo <- cpfpo(Hist(time, ev)~ age + creat,
data = mgus, tis=seq(10, 30, 0.3),
w=rep(1,67))
## and plot the odds-ratios
if(require("lattice")) {
xyplot(fit.cpfpo, scales = list(relation = "free"), layout = c(3, 1))
}
```

or the pseudo-values approach

```
data(mgus)
cutoffs <- quantile(mgus$time, probs = seq(0, 1, 0.05))[-1]
## with fancy variance estimation
fit1 <- pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE)
summary(fit1)
## with jackknife variance estimation
fit2 <- pseudocpf(Hist(time, ev) ~ age + creat, mgus, id = id,
timepoints = cutoffs, corstr = "independence",
scale.value = TRUE, jack = TRUE)
summary(fit2)
}
```

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