c(person("Yang", "Lu", role = c("aut", "cre"), email =
"yang.lu@williams.edu"), person("David", "Kane", role =
c("aut", "cre"), email = "dave.kane@gmail.com"))

c(person("Yang", "Lu", role = c("aut", "cre"),
email = "yang.lu2014@gmail.com"),
person("David", "Kane", role = c("aut"),
email = "dave.kane@gmail.com"))
A package that provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.

It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.

A package that provides tools for conducting performance
attribution for equity portfolios. The package uses two
methods: the Brinson method and a regression-based analysis.

PAC-Bayesian Estimation and Prediction in Sparse Additive
Models.
This package is intended to perform estimation and
prediction in high-dimensional additive models, using a sparse
PAC-Bayesian point of view and a MCMC algorithm. The method is
fully described in Guedj and Alquier (2012), 'PAC-Bayesian
Estimation and Prediction in Sparse Additive Models'.

This package is intended to perform estimation and
prediction in high-dimensional additive models, using a sparse
PAC-Bayesian point of view and a MCMC algorithm. The method is
fully described in Guedj and Alquier (2013), 'PAC-Bayesian
Estimation and Prediction in Sparse Additive Models',
Electronic Journal of Statistics, 7, 264--291.

Functions to easily convert data to binary formats other programs/machines can understand.

Functions to convert multiple data type values to/from raw bytes

Author: Christophe Genolini (and some helpfull readers...)
This package comes to illustrate the book "Petit Manuel de
Programmation Orientee Objet sous R"

Christophe Genolini and some reader that send useful comments
This package comes to illustrate the tutorial "S4: From Idea To Package"

Decision Analysis Modelling Package with Parameters Estimation
Ability from Individual Patient Level Data
c(person("Sheeja", "Manchira Krishnan", , "sheejamk@gmail.com", c("aut", "cre")))
A collection of functions to construct Markov model for model-based cost-effectiveness analysis.
This includes creating Markov model (both time homogenous and time dependent models), decision analysis,
sensitivity analysis (deterministic and probabilistic). The package allows estimation of parameters for
the Markov model from a given individual patient level data, provided the data file follows some standard
data entry rules.
stringr, data.table, reshape2, rlang, stats, lme4, flexsurv,
survminer, SurvRegCensCov, survival, MASS, systemfit,
IPDFileCheck, valueEQ5D, car, ggplot2, grDevices, lmtest,
broom, effects, gvlma, methods, relaimpo, tm
ISLR, foreign, dplyr, knitr, rmarkdown, covr, testthat