pearson7 supports maximum likelihood inference for the
Pearson VII distribution with shape parameter 3/2 and free
location and scale parameters. This distribution is relevant
when estimating the velocity of processive motor proteins with
random detachment.

Prediction Error Curves for Survival Models
Validation of predicted surival probabilities using inverse weighting and resampling.

Validation of predicted surival probabilities using
inverse weighting and resampling.

timereg, randomSurvivalForest, party, rpart, cmprsk, foreach,
doMC

Validation of predicted survival probabilities using
inverse weighting and resampling.

prodlim (>= 1.1.3), survival, rms, foreach
timereg, randomSurvivalForest, party, rpart, cmprsk, doMC,
CoxBoost

timereg, randomForestSRC, party, rpart, cmprsk, doMC,
CoxBoost, crrstep, riskRegression

Validation of predicted survival probabilities using inverse
weighting and resampling.
timereg, randomForestSRC, party, cmprsk, rpart, doMC,
CoxBoost, crrstep, riskRegression, lava, Hmisc

Prediction Error Curves for risk prediction models in survival
analysis
Validation of risk predictions obtained from survival models and
competing risk models based on censored data using inverse weighting and
cross-validation.
timereg, randomForestSRC, party, cmprsk (>= 2.2-7), rpart,
doMC, CoxBoost, crrstep, riskRegression (>= 1.0.8), lava, Hmisc
(>= 3.14-4)

Prediction Error Curves for Risk Prediction Models in Survival
Analysis

randomForestSRC, party, cmprsk (>= 2.2-7), rpart, doMC,
CoxBoost, crrstep, riskRegression (>= 1.0.8), lava (>= 1.4.1),
Hmisc (>= 3.14-4)

randomForestSRC, party, cmprsk (>= 2.2-7), rpart, CoxBoost,
crrstep, riskRegression (>= 1.0.8), lava (>= 1.4.1), Hmisc (>=
3.14-4)

Validation of risk predictions obtained from survival models and
competing risk models based on censored data using inverse weighting and
cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.
randomForestSRC, party, cmprsk (>= 2.2-7), rpart, crrstep,
Hmisc (>= 3.14-4)

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.