1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
Source: haskell-statistics
Maintainer: Debian Haskell Group <pkg-haskell-maintainers@lists.alioth.debian.org>
Uploaders:
Joachim Breitner <nomeata@debian.org>,
Priority: extra
Section: haskell
Build-Depends:
cdbs,
debhelper (>= 9),
ghc (>= 8),
ghc-prof,
haskell-devscripts (>= 0.13),
libghc-aeson-dev (>= 0.6.0.0),
libghc-aeson-prof,
libghc-erf-dev,
libghc-erf-prof,
libghc-math-functions-dev (>= 0.1.5.2),
libghc-math-functions-prof,
libghc-monad-par-dev (>= 0.3.4),
libghc-monad-par-prof,
libghc-mwc-random-dev (>= 0.13.0.0),
libghc-mwc-random-prof,
libghc-primitive-dev (>= 0.3),
libghc-primitive-prof,
libghc-vector-algorithms-dev (>= 0.4),
libghc-vector-algorithms-prof,
libghc-vector-binary-instances-dev (>= 0.2.1),
libghc-vector-binary-instances-prof,
libghc-vector-dev (>= 0.10),
libghc-vector-prof,
Build-Depends-Indep:
ghc-doc,
libghc-aeson-doc,
libghc-erf-doc,
libghc-math-functions-doc,
libghc-monad-par-doc,
libghc-mwc-random-doc,
libghc-primitive-doc,
libghc-vector-algorithms-doc,
libghc-vector-binary-instances-doc,
libghc-vector-doc,
Standards-Version: 3.9.8
Homepage: https://github.com/bos/statistics
Vcs-Browser: https://anonscm.debian.org/cgit/pkg-haskell/DHG_packages.git/tree/p/haskell-statistics
Vcs-Git: https://anonscm.debian.org/git/pkg-haskell/DHG_packages.git
Package: libghc-statistics-dev
Architecture: any
Depends:
${haskell:Depends},
${misc:Depends},
${shlibs:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Provides:
${haskell:Provides},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
This library provides a number of common functions and types useful
in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
.
The library's facilities can be divided into three broad categories:
.
Working with widely used discrete and continuous probability
distributions. (There are dozens of exotic distributions in use; we
focus on the most common.)
.
Computing with sample data: quantile estimation, kernel density
estimation, bootstrap methods, regression and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
${haskell:Blurb}
Package: libghc-statistics-prof
Architecture: any
Depends:
${haskell:Depends},
${misc:Depends},
${shlibs:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Provides:
${haskell:Provides},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
This library provides a number of common functions and types useful
in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
.
The library's facilities can be divided into three broad categories:
.
Working with widely used discrete and continuous probability
distributions. (There are dozens of exotic distributions in use; we
focus on the most common.)
.
Computing with sample data: quantile estimation, kernel density
estimation, bootstrap methods, and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
${haskell:Blurb}
Package: libghc-statistics-doc
Architecture: all
Section: doc
Depends:
${haskell:Depends},
${misc:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
This library provides a number of common functions and types useful
in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
.
The library's facilities can be divided into three broad categories:
.
Working with widely used discrete and continuous probability
distributions. (There are dozens of exotic distributions in use; we
focus on the most common.)
.
Computing with sample data: quantile estimation, kernel density
estimation, bootstrap methods, and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
${haskell:Blurb}
|