Changes in Version 1.2-0 o The functions closedp.mX(), closedp.h() and profile.CI() are now deprecated. They are replaced by the new function closedpCI.t() which simultaneously fits a loglinear model specified by the user and computes the multinomial profile likelihood confidence interval for the abundance estimation. o closedpCI() uses the R functions optimize() and uniroot() to obtain the profile likelihood confidence interval. The deprecated function profile.CI() used its own homemade algorithm. o The new method plotCI(), defined for the class 'closedpCI', allows to plot a profile likelihood confidence interval. This plot used to be generated automatically by profileCI(). o The plot() method for class 'closedpCI' traces the scatterplot of the Pearson residuals in terms of fi (number of units captured i times) for the customized model. o closedp() is now named closedp.t() o closedp.t() now identifies the non-convergent model in the print. Also, its returned list contains an element named converged. This is a logical vector indicating for each model whether it converged or not. o The new argument 'trace' of closedp.t() allows to print a note for each model while the function runs. It is useful to identify which model is associated to a warning. o In closedp.t(), some bugs were fixed in the correction for negative eta parameters in Chao's models when 'neg=TRUE' o In closedp.t(), the estimation of capture probabilities for Mth models was incorrect, it has been corrected. o In closedp.t(), the returned list has changed. It now has only one glm element, this element is a list of the glm objects for each model. Also the output now features an element named 'parameters' which is a list of the parameter estimates for each model. o The new plot() method for class 'closedp' produces scatterplots of the Pearson residuals in terms of fi (number of units captured i times) for the heterogeneous models Mh Poisson2, Mh Darroch and Mh Gamma3.5 if they converged o The boxplot() method for class 'closedp' does not trace boxplots for the non-convergent models anymore. o A new heterogeneity model has been added to the package : the gamma model. This means that closedp() has two more models in its output table, Mh Gamma3.5 and Mth Gamma3.5. Also the 'h' argument of closed population functions and the 'vh' argument of robust design functions accept the value "Gamma". o The 'a' argument of closed population functions and the 'va' argument of robust design functions are renamed 'theta' and 'vtheta' because the gamma model was added to the package and 'a' was referring to the Poisson heterogeneity model. 'theta' is a more general notation. o Functions for closed populations now work with only 2 capture occasions (non-helpful errors were previously produced). In that case, only models M0, Mt and Mb can be fitted. o The new functions closedp.0() and closedpCI.0() are equivalent to closedp.t() and closedpCI.t() but they fit model using the number of units capture i times instead of the frequencies of the observable capture histories. Consequently, they fit fewer models than the .t functions, but they work with much larger data set. o closedp.0() has the same methods as closedp.t() (print(), plot() and boxplot()), closedpCI.0() has the same methods as closedpCI.t() (print(), plotCI(), plot() and boxplot()). o The new .0 functions accept a new data format. The argument 'dtype', if set to "nbcap", allows the user to give the numbers of captures instead of the full capture histories. o In the new .0 functions, the argument 't' is required if 'dtype' takes the value 'nbcap'. It represents the number of capture occasions in the experiment. o closedp.bc() now computes bias corrected estimates for a model specified using the arguments 'm', 'h' and 'theta' as with the closedpCI functions. o Bias correction is now possible for any customized Mh model. o For models 'M0' and 'Mh', closedp.bc() accepts a data matrix 'X' in the format 'dtype' equals to "nbcap". o In closedp.bc(), the computations for model Mt have changed. The abundance estimate for the Mt model when t=2 has Chapman bias correction and a standard error derived from Seber and Wittes variance estimate. For t>2, closedp.bc() implements the bias correction of Rivest and Levesque (2001). The estimate for N and its variance are calculated by solving an estimating equation as proposed in Seber (1982), not by fitting a Poisson regression. This approach works for large values of t. o The 't0' argument of closedp.0(), closedpCI.0() and closedp.bc() allows to fit models considering only the frequencies of units captured 1 to t0 times. The frequencies of units caught more than t0 times are given their own parameters in the loglinear model. o descriptive() now accepts the new data formats, therefore it has two new arguments: 'dtype' and 't'. o The plot method for class 'descriptive' now traces only the fi plot if 'dtype' took the value "nbcap" in the call to descriptive(). o The documentation for periodhist() is now more general, it talks about merging capture occasions. o The new argument 'drop' of perdiohist() allows to omit from the output data set the unobserved capture histories having a frequency of 0. o histpos.0() now works when 'vt' is a scalar. o histfreq.t() and histfreq.0() are now internal functions. o Some bugs were fixed in the robustd.t and robustd.0 functions : they now work when vh contains R functions and when the data set contains only two primary periods. o A NAMESPACE has been added to the package, therefore internal functions are now hidden (e.g. Xclosedp(), Xomega() and Zdelta()) o The documentation has been updated. o The paper in inst/doc is now a vignette. Changes in Version 1.1 o Bug fixes for R 2.7.0. Changes in Version 1.0 o First CRAN release of the Rcapture package.