No packages match

MomTrunc - Moments of Folded and Doubly Truncated Multivariate Distributions

It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.

Last updated

generation-algorithmsmomentsprobability-statisticsopenblascpp

4.74 score 8 dependents 23 scripts 943 downloads

ModalForecast - Parametric Modal ARIMA Models using the SKD Family

Implements parametric modal ARIMA models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix.

Last updated

3.40 score

ModalCens - Parametric Modal Regression with Right Censoring

Implements parametric modal regression for continuous positive distributions of the exponential family and beyond (e.g., Log-Logistic, Birnbaum-Saunders) under right censoring. Provides functions to link the conditional mode to a linear predictor using alternative parameterizations. Includes maximum likelihood estimation via numerical optimization, asymptotic inference based on the observed Fisher information matrix, and model diagnostics using randomized quantile residuals. See Galarza and Lachos (2026) <doi:10.48550/arXiv.2603.07099>.

Last updated

3.00 score 426 downloads

ald - The Asymmetric Laplace Distribution

It provides the density, distribution function, quantile function, random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for the three parameter Asymmetric Laplace Distribution defined in Koenker and Machado (1999). This is a special case of the skewed family of distributions available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.

Last updated

2.76 score 1 stars 6 dependents 32 scripts 471 downloads

Opportunistic - Routing Distribution, Broadcasts, Transmissions and Receptions in an Opportunistic Network

Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.

Last updated

2.70 score 3 scripts 151 downloads

lqr - Robust Linear Quantile Regression

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Last updated

1.78 score 1 stars 2 dependents 9 scripts 355 downloads

ModalForecast - Parametric Modal ARIMA Models using the SKD Family

Implements parametric modal Autoregressive Integrated Moving Average (ARIMA) models utilizing the Skewed Distribution (SKD) family. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The conditional mode is parameterized and optimized via maximum likelihood using analytical gradients. Includes comprehensive residual diagnostics, robustness options (heavy tails, asymmetry), robust parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) <doi:10.1002/sta4.140>.

Last updated

1.70 score

qrNLMM - Quantile Regression for Nonlinear Mixed-Effects Models

Quantile regression (QR) for Nonlinear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference result is for the fixed-effects and variance components. It also provides prediction and graphical summaries for assessing the algorithm convergence and fitting results.

Last updated

1.30 score 2 stars 6 scripts 634 downloads

qrLMM - Quantile Regression for Linear Mixed-Effects Models

Quantile regression (QR) for Linear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

Last updated

1.08 score 1 stars 12 scripts 251 downloads

endtoend - Transmissions and Receptions in an End to End Network

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Last updated

1.00 score 2 scripts 224 downloads

hopbyhop - Transmissions and Receptions in a Hop by Hop Network

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Last updated

1.00 score 2 scripts 144 downloads