New version 0.17.0.

Minor documentation glitch fixes.

configure.ac

@@ -25,7 +25,7 @@ dnl ***************************************************************************
 # <mclasen> on the unstable (ie master), interface age = 0
 
 m4_define([numcosmo_major_version], [0])
-m4_define([numcosmo_minor_version], [16])
+m4_define([numcosmo_minor_version], [17])
 m4_define([numcosmo_micro_version], [0])
 m4_define([numcosmo_interface_age], [0])
 m4_define([numcosmo_binary_age],

numcosmo/math/ncm_data.c

@@ -32,8 +32,8 @@
  * building a statistical analysis. Every implementation of #NcmData envolves
  * the methods described in #NcmDataClass.
  *
- * A #NcmData must implement, at least, the method #NcmDataClass.m2lnL_val or
- * #NcmDataClass.leastsquares_f to perform respectively likelihood or least
+ * A #NcmData must implement, at least, the method ncm_data_m2lnL_val() or
+ * ncm_data_leastsquares_f() to perform respectively likelihood or least
  * squares analysis.
  *
  */

numcosmo/math/ncm_data.h

@@ -53,34 +53,6 @@ G_DECLARE_DERIVABLE_TYPE (NcmData, ncm_data, NCM, DATA, GObject)
  */
 typedef void (*NcmDataFisherMatrix) (NcmData *data, NcmMSet *mset, NcmMatrix **IM);
 
-/**
- * NcmDataClass:
- * @bootstrap: sets whenever the #NcmData implementations supports bootstrap.
- * @get_length: return the length associated to the #NcmData object.
- * @get_dof: return the effective degrees of freedom related to the #NcmData
- * statistics (likelihood or $\chi^2$) this number does not represent
- * necessarily the number of data points.
- * @begin: perform any model independent pre-calculation.
- * @prepare: perform any model dependent pre-calculation.
- * @resample: resample data from the models in #NcmMSet.
- * @leastsquares_f: calculates the least squares $\vec{f}$ vector, i.e.,
- * $\chi^2 \equiv \vec{f}\cdot\vec{f}$.
- * @leastsquares_J: calculates the least squares $\vec{f}$ vector derivatives
- * with respect to the free parameter of @mset.
- * @leastsquares_f_J: calculates both least squares vector and its derivatives.
- * @m2lnL_val: evaluate the minus two times the natural logarithm of the
- * likelihood, i.e., $-2\ln(L)$.
- * @m2lnL_grad: evaluate the gradient of $-2\ln(L)$ with respect to the free
- * parameters in @mset.
- * @m2lnL_val_grad: evaluate the value and the gradient of $-2\ln(L)$.
- * @mean_vector: evaluate the Gaussian mean (approximation or not)
- * @inv_cov_UH: evaluate the Gaussian inverse covariance matrix (approximation or not)
- * @fisher_matrix: calculates the Fisher matrix (based on a Gaussian approximation when it is the case)
- *
- * Virtual table for the #NcmData abstract class.
- *
- *
- */
 struct _NcmDataClass
 {
   /*< private >*/

numcosmo/math/ncm_data_gauss_cov_mvnd.c

@@ -26,7 +26,7 @@
 
 /**
  * SECTION:ncm_data_gauss_cov_mvnd
- * @title: NcmDataGaussCov
+ * @title: NcmDataGaussCovMVND
  * @short_description: Multivariate Normal Distribution -- covariance provided.
  *
  * Multivariate Normal distribution which uses the covariance matrix as input.

numcosmo/math/ncm_data_gauss_diag.c

@@ -572,7 +572,7 @@ ncm_data_gauss_diag_get_size (NcmDataGaussDiag *diag)
 
 /**
  * ncm_data_gauss_diag_peek_mean:
- * diag: a #NcmDataGaussDiag
+ * @diag: a #NcmDataGaussDiag
  *
  * Returns: (transfer none): the current data mean #NcmVector.
  */
@@ -586,7 +586,7 @@ ncm_data_gauss_diag_peek_mean (NcmDataGaussDiag *diag)
 
 /**
  * ncm_data_gauss_diag_peek_std:
- * diag: a #NcmDataGaussDiag
+ * @diag: a #NcmDataGaussDiag
  *
  * Returns: (transfer none): the current data standard deviation #NcmVector.
  */

numcosmo/math/ncm_dataset.c

@@ -722,9 +722,9 @@ ncm_dataset_get_info (NcmDataset *dset)
  * ncm_dataset_has_leastsquares_f:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a leastsquares_f() method.
+ * Whether all the #NcmData in @dset have a ncm_data_leastsquares_f() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a leastsquares_f() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_leastsquares_f() method.
  */
 gboolean
 ncm_dataset_has_leastsquares_f (NcmDataset *dset)
@@ -753,9 +753,9 @@ ncm_dataset_has_leastsquares_f (NcmDataset *dset)
  * ncm_dataset_has_leastsquares_J:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a leastsquares_J() method.
+ * Whether all the #NcmData in @dset have a ncm_data_leastsquares_J() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a leastsquares_J() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_leastsquares_J() method.
  */
 gboolean
 ncm_dataset_has_leastsquares_J (NcmDataset *dset)
@@ -784,9 +784,9 @@ ncm_dataset_has_leastsquares_J (NcmDataset *dset)
  * ncm_dataset_has_leastsquares_f_J:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a leastsquares_f_J() method.
+ * Whether all the #NcmData in @dset have a ncm_data_leastsquares_f_J() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a leastsquares_f_J() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_leastsquares_f_J() method.
  */
 gboolean
 ncm_dataset_has_leastsquares_f_J (NcmDataset *dset)
@@ -815,9 +815,9 @@ ncm_dataset_has_leastsquares_f_J (NcmDataset *dset)
  * ncm_dataset_has_m2lnL_val:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a m2lnL_val() method.
+ * Whether all the #NcmData in @dset have a ncm_data_m2lnL_val() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a m2lnL_val() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_m2lnL_val() method.
  */
 gboolean
 ncm_dataset_has_m2lnL_val (NcmDataset *dset)
@@ -846,9 +846,9 @@ ncm_dataset_has_m2lnL_val (NcmDataset *dset)
  * ncm_dataset_has_m2lnL_grad:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a m2lnL_grad() method.
+ * Whether all the #NcmData in @dset have a ncm_data_m2lnL_grad() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a m2lnL_grad() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_m2lnL_grad() method.
  *
  */
 gboolean
@@ -878,9 +878,9 @@ ncm_dataset_has_m2lnL_grad (NcmDataset *dset)
  * ncm_dataset_has_m2lnL_val_grad:
  * @dset: a #NcmDataset
  *
- * Whether all the #NcmData in @dset have a m2lnL_val_grad() method.
+ * Whether all the #NcmData in @dset have a ncm_data_m2lnL_val_grad() method.
  *
- * Returns: %TRUE if all the #NcmData in @dset have a m2lnL_val_grad() method.
+ * Returns: %TRUE if all the #NcmData in @dset have a ncm_data_m2lnL_val_grad() method.
  *
  */
 gboolean