Showing 9 of total 9 results (show query)
pchausse
gmm:Generalized Method of Moments and Generalized Empirical Likelihood
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
2 stars 8.75 score 304 scripts 65 dependents
ajbass
lit:Latent Interaction Testing for Genome-Wide Studies
Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.
Maintained by Andrew Bass. Last updated 2 years ago.
2 stars 4.15 score 14 scripts
hdakpo
sfaR:Stochastic Frontier Analysis Routines
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021) <doi:10.1111/agec.12683>. Several possibilities in terms of optimization algorithms are proposed.
Maintained by K Hervé Dakpo. Last updated 5 months ago.
4 stars 4.08 score 9 scripts 1 dependents
ycroissant
descstat:Tools for Descriptive Statistics
A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.
Maintained by Yves Croissant. Last updated 4 years ago.
2 stars 3.00 score 1 scripts
pettermostad
lestat:A Package for Learning Statistics
Some simple objects and functions to do statistics using linear models and a Bayesian framework.
Maintained by Petter Mostad. Last updated 7 years ago.
2.28 score 64 scripts 1 dependents
cran
PerMallows:Permutations and Mallows Distributions
Includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence).
Maintained by Ekhine Irurozki. Last updated 2 months ago.
1 stars 1.00 score
haoluns
clogitboost:Boosting Conditional Logit Model
A set of functions to fit a boosting conditional logit model.
Maintained by Haolun Shi. Last updated 9 years ago.
1.00 score 5 scripts