Modelización y diseño de experimentos
Modelling with differential equations, fitting coefficients, convolution, and more, with application for modelling Biokinetic Systems. It included the current ICRP biokinetic models. It can be applied in pharmacokinetic, internal dosimetry, bioassay evaluations, nuclear medicine and more. Run online using a webbrowser (http://ehe.usal.es/webMathematica/)
Estadística espacial, Modelos lineales mixtos, Análisis de pedigrí
Statistical methods for forest genetic resources analysts.
Analysis of the dynamic of fish populations
In recent years, there has been an increasing research effort on developing methods that can generally improve the reliability of stock assessments in data-limited situations. Consequently, several data-limited assessment methods have been proposed, and surplus production models (SPMs) were among the assessment methodologies recommended for this purpose, which only requires time series of an index of relative biomass and catch data. SPMs are one of the the simplest analytical methods available for providing a full stock assessment, which estimates the changes in the biomass as afunction of the biomass of the previous year, the surplus production and the catches.
A well-known and widely used SPM is ASPIC (A Stock-Production ModelIncorporating Covariate; see Prager (1992) and (1994)). ASPIC can be fitted through ASPIC Suite program, some executables availables at(http://www.mhprager.com/aspic.html). A disadvantage of ASPIC suite program is thatthe input file, which contains the input data and the available prior values of the modelparameters, must be created manually, and then the executable must be openned to fitthe model. Hence, if we are interesting on running slighly different ASPIC’s varying theprior values of the model parameters, for example, the previous procedure has to berepeated manually several times.
For solving the mentioned problem, we have developed connectASPIC (available at https://github.com/IMPRESSPROJECT/connectASPIC), an R package which fits ASPIC in R connecting with Version 7 of the ASPIC Suite program. For this aim, our package contains three functions: the first one creates and fills up an input file(.a7inp) for ASPIC program, once the input file is available our second function callsfrom R the ASPIC executable to fit ASPIC based on the created input file, and finallythe resulting output file is reading in R using our last function.
Therefore, using connectASPIC, the SPM can be fitted automatically, and hence studies above the effect on its performance depending of the input information can be carriedout easily.
Estimación de medidas basimétricas en entorno forestal
Process automation of Terrestrial Laser Scanner (TLS) point cloud data derived from single scans. ‘FORTLS’ enables (i) detection of trees and estimation of diameter at breast height (dbh), (ii) estimation of some stand variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories (FIs) at stand level and (iv) optimization of plot design for combining TLS data and field measured data.
An extension of package geoR that works with cost-based distances.
This package implements four different GSA methods for combining individual p-values of a set of SNPs. Each method provides a p-value for a joint test of association between the phenotype and the specified set of genetic variants. Since the SNPs in a set may follow different modes of inheritance, previously to the GSA, a global test for the best inheritance model (dominant, recessive, log-additive, and co-dominant) is performed on every SNP. The permutational p-value of the best model is obtained.
The four implemented methods are:
Modelo de regresión para datos de muerte por enfermedad progresiva:
Modelado de efectos de regresión para probabilidades de transición en un modelo progresivo enfermedad-muerte.
Cartografía y vigilancia epidemiológica de enfermedades
Produce an epidemiological risk map by weighting multiple risk factors.
Dealing with with uncertainty for analysing the dynamic of exploited fish populations
The analysis of the dynamic of a population has become a fundamental tool in ecology, conservation biology, and particularly in fisheries science to assess the status of exploited resources. Uncertainty is an inherent component in fishery systems that makes difficult taking management decisions. Here, we present Rfishpop (available on https://github.com/IMPRESSPROJECT/Rfishpop) a package to deal with with uncertainty for analyzing exploited populations in R. More precisely, Rfishpop package address such aims implementing a completed Management Strategy Evaluation (MSE) cycle which is a simulated approach explicitly designed to identify fishery rebuilding strategies and ongoing harvest strategies that are robust to uncertainty and natural variation (Punt et al. 2016 and Kell et al., 2007).
A prototypical MSE incorporates a number of interlinked model structures. The steps for a MSE cycle, are:
In its current state, the package includes tools to simulate the real dynamics of a fishery using a generic age-structured operating model. The OM models a biological system with recruitment, growth, maturity and natural mortality and a fishery system were fishing intensity and selection. This allows to implement structural uncertainty having different options for each process and natural stochasticity playing with variability in these processes. Once the exploited population has been generated through the OM, the package also contains a set of methods to estimate biological reference points as Maximum Sustainable Yield (MSY) reference points (Hart and Reynolds, 2002). These points allow to identify management targets in terms of fishing intensity, population status and yield. The package also contains statistical methods for sampling data from the OM simulating sampling error, which is another source of uncertainty in fishery management. These methods provides different data types which can suit different assessment methods, from simple data-limited methods to more complex age or length-structured methods (examples of assessment models can be found in Chapters 6 and 7 of Haddon, 2002).
As we mentioned above, the data obtaining from the sample functions are passed to the assessment model. Our package does not develop any new assessment model as the idea is to implement the already existents ones. The package contains specific functions to change the format of the data reported by Rfishpop into the required format of the assessment model function. Finally, the package contains functions to implement the resulting management action, determined from the assessment and the HCR, projecting our exploited population through the years on based of catches or effort established by the management action. The described functions of Rfishpop package allow to verify the performance of management strategies or procedures in different settings generated from the OM. The package is also useful to check the performance of assessment models when some their assumptions are violated or some parameters are misspecificated. It is important to stand out that this package is an open project, future aims focus on introducing new posibilities at some steps of the MSE cycle and also improvements in some of the procedures already implemented.