• gbm

  • Referenced in 50 articles [sw07994]
  • Generalized Boosted Regression Models. This package implements extensions to Freund and Schapire’s AdaBoost algorithm ... Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial ... Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning...
  • normalp

  • Referenced in 16 articles [sw10540]
  • distribution function and the quantiles from an exponential power distribution and to generate pseudo-random ... linear regression models when we assume the random errors distributed according to an exponential power...
  • aster2

  • Referenced in 2 articles [sw32136]
  • Aster Models. Aster models are exponential family regression models for life history analysis. They ... zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic...
  • Bolasso

  • Referenced in 28 articles [sw31649]
  • bootstrap. We consider the least-square linear regression problem with regularization by the l1-norm ... present a detailed asymptotic analysis of model consistency of the Lasso. For various decays ... asymptotic equivalents of the probability of correct model selection (i.e., variable selection). For a specific ... that should enter the model with probability tending to one exponentially fast, while it selects...
  • ECOTOOL

  • Referenced in 5 articles [sw12435]
  • Exponential Smoothing, Unobserved Components, ARX, ARMAX, Transfer Function, Dynamic Regression and Distributed Lag models...
  • twexp

  • Referenced in 0 articles [sw37546]
  • Fitting exponential regression models with two-way fixed effects. In this article, we introduce ... Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp...
  • N-way Toolbox

  • Referenced in 30 articles [sw12996]
  • Fitting multi-way PLS regression models; Fitting multi-way Tucker models; Fitting the generalized rank ... using a given model; Predicting the dependent variable(s) of PLS models; Performing multi ... Performing cross-validation of models; Calculating core consistency of PARAFAC models; Using additional diagnostic tools ... Perform rotations of core and models in Tucker models; Plus additional utility functions. In addition...
  • grpreg

  • Referenced in 6 articles [sw15799]
  • regularization path of linear or logistic regression models with grouped penalties. This includes group selection ... level selection methods such as the group exponential lasso, the composite MCP, and the group...
  • pmlr

  • Referenced in 1 article [sw21117]
  • MLEs in exponential family models to the multinomial logistic regression model with general covariate types...
  • ssym

  • Referenced in 5 articles [sw14396]
  • tools to fit a semi-parametric regression model suitable for analysis of data sets ... response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components ... model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated...
  • LDOD

  • Referenced in 1 article [sw20245]
  • Designs for some Nonlinear and Generalized Linear Models. this package provides functions for Finding Locally ... Michaelis-Menten, Exponential, Log-Linear, Emax, Richards, Weibull and Inverse Quadratic regression models and also...
  • MAIC

  • Referenced in 7 articles [sw19964]
  • Akaike’s Information Criterion in Linear Regression Analysis via Mixed Integer Nonlinear Program. Akaike ... model for a given set of data. We can determine the best statistical model ... Since we need to evaluate exponentially many candidates of the model by the minimization...
  • spatcounts

  • Referenced in 6 articles [sw13743]
  • health insurance. We consider spatial regression models for count data. We examine not only ... also the generalized Poisson capable of modeling over-dispersion, the negative Binomial as well ... distribution. We add random spatial effects for modeling spatial dependency and develop and implement MCMC ... CRAN. In an application the presented models are used to analyze the number of benefits...
  • AMIDST

  • Referenced in 5 articles [sw21741]
  • covers a wide range of conjugate exponential family models. We evaluate the proposed algorithm ... using several models (LDA, factor analysis, mixture of Gaussians and linear regression models). Our approach...
  • MIXREG

  • Referenced in 5 articles [sw24547]
  • effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model ... model can also be used for analysis of clustered data, where the mixed-effects model ... estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses ... diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished...
  • gmb

  • Referenced in 1 article [sw14565]
  • Generalized Boosted Regression Models. An implementation of extensions to Freund and Schapire’s AdaBoost algorithm ... Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial ... Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning...
  • MTS

  • Referenced in 5 articles [sw15485]
  • regression models with time series errors, augmented VAR models, and Error-correction VAR models ... integrated time series. For model specification, the package performs structural specification to overcome the difficulties ... models. The methods used for structural specification include Kronecker indices and Scalar Component Models ... volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving...
  • SimSCRPiecewise

  • Referenced in 1 article [sw16059]
  • data based on three piecewise exponential hazards, three true regression vectors and three matrices ... same). This simulates from the Semi-Markov model of Lee et al (2015) given patient...
  • HELARS

  • Referenced in 1 article [sw36942]
  • this process. Most notably, the Least Angle Regression algorithm, or LARS, is a computationally efficient ... that ranks the covariates of a linear model. The algorithm is further extended ... generalized linear model by using properties of the manifold of exponential families as dually flat ... call this the holonomic extended least angle regression algorithm, or HELARS. The algorithm was implemented...