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{
"name": "R-LMest",
"description": "Generalized Latent Markov Models"
},
{
"name": "R-lmm",
"description": "Linear Mixed Models"
},
{
"name": "R-LMMELSM",
"description": "Latent Multivariate Mixed Effects Location Scale Models"
},
{
"name": "R-LMMsolver",
"description": "Linear Mixed Model Solver"
},
{
"name": "R-LMMstar",
"description": "Repeated measurement models for discrete times"
},
{
"name": "R-lmodel2",
"description": "Model II regression"
},
{
"name": "R-LMoFit",
"description": "Advanced l-moment fitting of distributions"
},
{
"name": "R-lmom",
"description": "Functions related to L-moments"
},
{
"name": "R-lmomco",
"description": "L-moments, censored l-moments, trimmed l-moments, l-comoments and many distributions"
},
{
"name": "R-Lmoments",
"description": "L-moments and quantile mixtures"
},
{
"name": "R-lmtest",
"description": "Testing linear regression models"
},
{
"name": "R-lmtp",
"description": "Non-parametric causal effects of feasible interventions based on modified treatment policies"
},
{
"name": "R-LNPar",
"description": "Estimation and testing for a Lognormal-Pareto mixture"
},
{
"name": "R-lobstr",
"description": "Visualize R data structures with trees"
},
{
"name": "R-localgauss",
"description": "Estimation of local Gaussian parameters"
},
{
"name": "R-locateip",
"description": "Locate IP addresses with ip-api"
},
{
"name": "R-locatexec",
"description": "Detection and localization of executable files"
},
{
"name": "R-locfdr",
"description": "Computation of local false discovery rates"
},
{
"name": "R-locfit",
"description": "Local regression, likelihood and density estimation"
},
{
"name": "R-locits",
"description": "Test of stationarity and localized autocovariance"
},
{
"name": "R-locpol",
"description": "Kernel Local Polynomial regression"
},
{
"name": "R-locpolExpectile",
"description": "Local Polynomial Expectile regression"
},
{
"name": "R-loder",
"description": "Dependency-free access to PNG image files"
},
{
"name": "R-logcondens",
"description": "Estimate a log-concave probability density from IID observations"
},
{
"name": "R-logcondiscr",
"description": "Estimate a log-concave probability mass function from discrete i.i.d. observations"
},
{
"name": "R-logger",
"description": "Lightweight, modern and flexible logging utility"
},
{
"name": "R-logging",
"description": "R Logging package"
},
{
"name": "R-logiBin",
"description": "Binning variables to use in logistic regression"
},
{
"name": "R-LogicReg",
"description": "Logic Regression"
},
{
"name": "R-logistf",
"description": "Firth’s bias-reduced logistic regression"
},
{
"name": "R-logitnorm",
"description": "Functions for the logitnormal distribution"
},
{
"name": "R-logitr",
"description": "Logit models w/Preference & WTP space utility parameterizations"
},
{
"name": "R-loglognorm",
"description": "Double log-normal distribution functions"
},
{
"name": "R-logmult",
"description": "Log-multiplicative models, including association models"
},
{
"name": "R-lognorm",
"description": "Functions for the lognormal distribution"
},
{
"name": "R-logNormReg",
"description": "Log-normal linear regression"
},
{
"name": "R-logOfGamma",
"description": "Natural logarithms of the gamma function for large values"
},
{
"name": "R-logr",
"description": "Functions to help create log files"
},
{
"name": "R-logspline",
"description": "Routines for logspline density estimation"
},
{
"name": "R-lokern",
"description": "Kernel regression smoothing with local or global plug-in bandwidth"
},
{
"name": "R-lolog",
"description": "Latent order logistic graph models"
},
{
"name": "R-lolR",
"description": "Linear Optimal Low-Rank projection"
},
{
"name": "R-LOMAR",
"description": "Read, register and compare point sets from single molecule localization microscopy"
},
{
"name": "R-longit",
"description": "High-dimensional longitudinal data analysis using MCMC"
},
{
"name": "R-longitudinalData",
"description": "Tools for longitudinal data and joint longitudinal data"
},
{
"name": "R-longmemo",
"description": "Statistics for long-memory processes"
},
{
"name": "R-LongMemoryTS",
"description": "Long-memory time series"
},
{
"name": "R-loo",
"description": "Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models."
},
{
"name": "R-lookout",
"description": "Leave-one-out kernel density estimates for outlier detection"
},
{
"name": "R-LOPART",
"description": "Labelled Optimal Partitioning"
}
]
}