- Valgrind error fixed

- For CRAN release 1.4.0’s new features see features described for versions 1.3.2-1.3.3

- For bug fixes to CRAN release 1.4.0 see versions 1.3.2-1.3.3

- The n.init option has been improved, so that it stops if no improved fit has been found after n.init.max (defaults to 10) iterations.
- Row names from the data now carry over to the site scores, so that they can be displayed in ordiplot

Memory allocation problem in development version fixed

Diagonal elements of loading matrix ‘theta’ fixed for fourth corner model

Bug in ‘predict’ for random slopes fixed, occurred when new x-covariate values were given

Ordination with predictors (num.RR,num.lv.c) is now implemented with constrained optimization routines (alabama,nloptr) as long as the canonical coefficients are treated as fixed-effects. This follows from the necessary identifiability constraints.

The reduced-rank approximated predictor slopes of a multivariate regression can now be plotted (with confidence intervals) using coefplot. Not available yet for quadratic effects.

Separate checks are put in place to warn users if the constraints on the canonical coefficients (orthogonality of the columns) have not converged.

Separate checks are put in place to warn users if the coefficients of a quadratic model have not converged

Canonical coefficients in ordination with predictors (num.RR,num.lv.c) can now be treated as random-effects using the ‘randomB’ argument. For the moment, all need to be either random or fixed, no mixing. Prediction intervals can be retrieved with the getPredictErr function.

An extended version of the spider dataset has been made available

Added an option to magnify the x-axis labels in coefplot

Site names present as row labels in the response data are now shown in the ordination plot

The order of the quadratic coefficients was wrong when num.RR, num.lv, and num.lv.c were all used in the same model.

Fixed a bug in the calculation of starting values for constrained ordination (num.RR) where the residuals were not re-calculated if num.lv.c>0

Fixed a bug in coefplot for when only one predictor was included in the model

Fixed a bug that would prevent using a gllvm with quadratic response model as starting values for another model

Changed import/export of various functions as requested in github issue #65

Various minor tweaks to the summary function

Structured row parameters are implemented, including a possibility for between or within group correlations for random row effects.

Constrained ordination model is implemented.

NB and binomial (with probit and logit) response model implemented using extended variational approximation method.

- Vignettes are removed from the CRAN version of the package, can be seen at the package’s website only.

Quadratic latent variables allowed, that is term - u_i’D_j u_i can be included in the model using ‘quadratic = TRUE’. In addition, functions ‘optima()’, ‘tolerances()’ and ‘gradient.length()’ included.

Beta response distribution implemented using Laplace approximation and extended variational approximation method.

Tweedie response model implemented using extended variational approximation method.

Ordinal model works now for ‘num.lv=0’.

Residual covariance adjustment added for gaussian family.

Estimation of the variances of random slopes of the X covariates didn’t work properly when ‘row.eff = FALSE’ or ‘row.eff = “fixed”’.

Problems occurred in calculation of the starting values for ordinal model.

Problems occurred in predict() and residuals(), when random slopes for X covariates were included.

Problems occurred in predict() when new X covariates were given.

Problems occurred in predictLVs() for fourth corner models.

Structured row parameters are implemented, including a possibility for between or within group correlations for random row effects.

Constrained ordination model is implemented.

NB and binomial (with probit and logit) response model implemented using extended variational approximation method.

- Vignettes are removed from the CRAN version of the package, can be seen at the package’s website only.

Quadratic latent variables allowed, that is term - u_i’D_j u_i can be included in the model using ‘quadratic = TRUE’. In addition, functions ‘optima()’, ‘tolerances()’ and ‘gradient.length()’ included.

Beta response distribution implemented using Laplace approximation and extended variational approximation method.

Tweedie response model implemented using extended variational approximation method.

Ordinal model works now for ‘num.lv=0’.

Residual covariance adjustment added for gaussian family.

Estimation of the variances of random slopes of the X covariates didn’t work properly when ‘row.eff = FALSE’ or ‘row.eff = “fixed”’.

Problems occurred in calculation of the starting values for ordinal model.

Problems occurred in predict() and residuals(), when random slopes for X covariates were included.

Problems occurred in predict() when new X covariates were given.

Problems occurred in predictLVs() for fourth corner models.